This glossary defines general machine learning terms, plus terms specific to TensorFlow.

## A

## A/B testing

A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.

## accuracy

The fraction of **predictions** that a
**classification model** got right. In
**multi-class classification**, accuracy
is defined as follows:

In **binary classification**, accuracy has
the following definition:

See **true positive** and
**true negative**. Contrast **accuracy** with
**precision** and
**recall**.

## action

In reinforcement learning, the mechanism by which the **agent**
transitions between **states** of the
**environment**. The agent chooses the action by using a
**policy**.

## activation function

A function (for example, **ReLU** or **sigmoid**)
that takes in the weighted sum of all of the inputs from the previous layer
and then generates and passes an output value (typically nonlinear) to the next
layer.

## active learning

A **training** approach in which the
algorithm *chooses* some of the data it learns from. Active learning
is particularly valuable when **labeled examples**
are scarce or expensive to obtain. Instead of blindly seeking a diverse
range of labeled examples, an active learning algorithm selectively seeks
the particular range of examples it needs for learning.

## AdaGrad

A sophisticated gradient descent algorithm that rescales the
gradients of each parameter, effectively giving each parameter
an independent **learning rate**. For a full explanation, see
this paper.

## agent

In reinforcement learning, the entity that uses a **policy**
to maximize expected **return** gained from transitioning
between **states** of the **environment**.

## agglomerative clustering

## anomaly detection

The process of identifying **outliers**. For example, if the mean
for a certain **feature** is 100 with a standard deviation of 10,
then anomaly detection should flag a value of 200 as suspicious.

## AR

Abbreviation for **augmented reality**.

## area under the PR curve

See **PR AUC (Area under the PR Curve)**.

## area under the ROC curve

See **AUC (Area under the ROC curve)**.

## artificial general intelligence

A non-human mechanism that demonstrates a *broad range* of problem solving,
creativity, and adaptability. For example, a program demonstrating artificial
general intelligence could translate text, compose symphonies, *and* excel at
games that have not yet been invented.

## artificial intelligence

A non-human program or model that can solve sophisticated tasks. For example, a program or model that translates text or a program or model that identifies diseases from radiologic images both exhibit artificial intelligence.

Formally, **machine learning** is a sub-field of artificial
intelligence. However, in recent years, some organizations have begun using the
terms *artificial intelligence* and *machine learning* interchangeably.

## attention

Any of a wide range of **neural network** architecture
mechanisms
that aggregate information from a set of inputs in a data-dependent manner. A
typical attention mechanism might consist of a weighted sum over a set of
inputs, where the **weight** for each input is computed by another
part of the neural network.

Refer also to **self-attention** and
**multi-head self-attention**, which are the
building blocks of **Transformers**.

## attribute

Synonym for **feature**. In fairness, attributes often refer to
characteristics pertaining to individuals.

## AUC (Area under the ROC Curve)

An evaluation metric that considers all possible
**classification thresholds**.

The Area Under the **ROC curve** is the probability that a classifier
will be more confident that a randomly chosen positive example is actually
positive than that a randomly chosen negative example is positive.

## augmented reality

A technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view.

## automation bias

When a human decision maker favors recommendations made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors.

## average precision

A metric for summarizing the performance of a ranked sequence of results.
Average precision is calculated by taking the average of the
**precision** values for each relevant result (each result in
the ranked list where the recall increases relative to the previous result).

See also **Area under the PR Curve**.

## B

## backpropagation

The primary algorithm for performing
**gradient descent** on
**neural networks**. First, the output values
of each node are calculated (and cached) in a forward pass.
Then, the **partial derivative**
of the error with respect to each parameter is calculated in a backward
pass through the graph.

## bag of words

A representation of the words in a phrase or passage, irrespective of order. For example, bag of words represents the following three phrases identically:

- the dog jumps
- jumps the dog
- dog jumps the

Each word is mapped to an index in a **sparse vector**, where
the vector has an index for every word in the vocabulary. For example,
the phrase *the dog jumps* is mapped into a feature vector with non-zero
values at the three indices corresponding to the words *the*, *dog*, and
*jumps*. The non-zero value can be any of the following:

- A 1 to indicate the presence of a word.
- A count of the number of times a word appears in the bag. For example,
if the phrase were
*the maroon dog is a dog with maroon fur*, then both*maroon*and*dog*would be represented as 2, while the other words would be represented as 1. - Some other value, such as the logarithm of the count of the number of times a word appears in the bag.

## baseline

A **model** used as a reference point for comparing how well another
model (typically, a more complex one) is performing. For example, a
**logistic regression model** might serve as a
good baseline for a **deep model**.

For a particular problem, the baseline helps model developers quantify the minimal expected performance that a new model must achieve for the new model to be useful.

## batch

The set of examples used in one **iteration** (that is, one
**gradient** update) of
**model training**.

See also **batch size**.

## batch normalization

**Normalizing** the input or output of the
**activation functions** in a
**hidden layer**. Batch normalization can
provide the following benefits:

- Make
**neural networks**more stable by protecting against**outlier**weights. - Enable higher
**learning rates**. - Reduce
**overfitting**.

## batch size

The number of examples in a **batch**. For example, the batch size
of **SGD** is 1, while the batch size of
a **mini-batch** is usually between 10 and 1000. Batch size is
usually fixed during **training** and **inference**;
however, **TensorFlow** does permit dynamic batch sizes.

## Bayesian neural network

A probabilistic **neural network** that accounts for
uncertainty in **weights** and outputs. A standard neural network
regression model typically **predicts** a scalar value;
for example, a model predicts a house price
of 853,000. By contrast, a Bayesian neural network predicts a distribution of
values; for example, a model predicts a house price of 853,000 with a standard
deviation of 67,200. A Bayesian neural network relies on
Bayes' Theorem
to calculate uncertainties in weights and predictions. A Bayesian neural
network can be useful when it is important to quantify uncertainty, such as in
models related to pharmaceuticals. Bayesian neural networks can also help
prevent **overfitting**.

## Bayesian optimization

A **probabilistic regression model**
technique for optimizing computationally expensive
**objective functions** by instead optimizing a surrogate
that quantifies the uncertainty via a Bayesian learning technique. Since
Bayesian optimization is itself very expensive, it is usually used to optimize
expensive-to-evaluate tasks that have a small number of parameters, such as
selecting **hyperparameters**.

## Bellman equation

In reinforcement learning, the following identity satisfied by the optimal
**Q-function**:

\[Q(s, a) = r(s, a) + \gamma \mathbb{E}_{s'|s,a} \max_{a'} Q(s', a'))\]

**Reinforcement learning** algorithms apply this
identity to create **Q-learning** via the following update rule:

\[Q(s,a) \gets Q(s,a) + \alpha \left[r(s,a) + \gamma \displaystyle\max_{\substack{a_1}} Q(s’,a’) - Q(s,a) \right] \]

Beyond reinforcement learning, the Bellman equation has applications to dynamic programming. See the Wikipedia entry for Bellman Equation.

## BERT (Bidirectional Encoder Representations from Transformers)

A model architecture for text **representation**. A trained
BERT model can act as part of a larger model for text classification or
other ML tasks.

BERT has the following characteristics:

- Uses the
**Transformer**architecture, and therefore relies on**self-attention**. - Uses the
**encoder**part of the Transformer. The encoder's job is to produce good text representations, rather than to perform a specific task like classification. - Is
**bidirectional**. - Uses
**masking**for**unsupervised training**.

BERT's variants include:

See Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing for an overview of BERT.

## bias (ethics/fairness)

1. Stereotyping, prejudice or favoritism towards some things, people, or groups over others. These biases can affect collection and interpretation of data, the design of a system, and how users interact with a system. Forms of this type of bias include:

**automation bias****confirmation bias****experimenter’s bias****group attribution bias****implicit bias****in-group bias****out-group homogeneity bias**

2. Systematic error introduced by a sampling or reporting procedure. Forms of this type of bias include:

Not to be confused with the bias term in machine learning models
or **prediction bias**.

## bias (math)

An intercept or offset from an origin. Bias (also known as the
**bias term**) is referred to as *b* or *w _{0}* in
machine learning models. For example, bias is the

*b*in the following formula:

Not to be confused with **bias in ethics and fairness**
or **prediction bias**.

## bigram

An **N-gram** in which N=2.

## bidirectional

A term used to describe a system that evaluates the text that both *precedes*
and *follows* a target section of text. In contrast, a
**unidirectional** system only
evaluates the text that *precedes* a target section of text.

For example, consider a **masked language model** that
must determine probabilities for the word(s) representing the underline in
the following question:

What is the _____ with you?

A unidirectional language model would have to base its probabilities only on the context provided by the words "What", "is", and "the". In contrast, a bidirectional language model could also gain context from "with" and "you", which might help the model generate better predictions.

## bidirectional language model

A **language model** that determines the probability that a
given token is present at a given location in an excerpt of text based on
the *preceding* and *following* text.

## binary classification

A type of **classification** task that outputs one
of two mutually exclusive **classes**. For example, a machine
learning model that evaluates email messages and outputs either "spam" or
"not spam" is a **binary classifier**.

## binning

See **bucketing**.

## BLEU (Bilingual Evaluation Understudy)

A score between 0.0 and 1.0, inclusive, indicating the quality of a translation between two human languages (for example, between English and Russian). A BLEU score of 1.0 indicates a perfect translation; a BLEU score of 0.0 indicates a terrible translation.

## boosting

A machine learning technique that iteratively combines a set of simple and
not very accurate classifiers (referred to as "weak" classifiers) into a
classifier with high accuracy (a "strong" classifier) by
**upweighting** the examples that the model is currently
misclassifying.

## bounding box

In an image, the (*x*, *y*) coordinates of a rectangle around an area of
interest, such as the dog in the image below.

## broadcasting

Expanding the shape of an operand in a matrix math operation to
**dimensions** compatible for that operation. For instance,
linear algebra requires that the two operands in a matrix addition operation
must have the same dimensions. Consequently, you can't add a matrix of shape
(m, n) to a vector of length n. Broadcasting enables this operation by
virtually expanding the vector of length n to a matrix of shape (m,n) by
replicating the same values down each column.

For example, given the following definitions, linear algebra prohibits A+B because A and B have different dimensions:

```
A = [[7, 10, 4],
[13, 5, 9]]
B = [2]
```

However, broadcasting enables the operation A+B by virtually expanding B to:

```
[[2, 2, 2],
[2, 2, 2]]
```

Thus, A+B is now a valid operation:

```
[[7, 10, 4], + [[2, 2, 2], = [[ 9, 12, 6],
[13, 5, 9]] [2, 2, 2]] [15, 7, 11]]
```

See the following description of broadcasting in NumPy for more details.

## bucketing

Converting a (usually **continuous**) feature into
multiple binary features called buckets or bins, typically based on value
range. For example, instead of representing temperature as a single
continuous floating-point feature, you could chop ranges of temperatures
into discrete bins. Given temperature data sensitive to a tenth of a degree,
all temperatures between 0.0 and 15.0 degrees could be put into one bin,
15.1 to 30.0 degrees could be a second bin, and 30.1 to 50.0 degrees could
be a third bin.

## C

## calibration layer

A post-prediction adjustment, typically to account for
**prediction bias**. The adjusted predictions and
probabilities should match the distribution of an observed set of labels.

## candidate generation

The initial set of recommendations chosen by a recommendation system. For
example, consider a bookstore that offers 100,000 titles. The candidate
generation phase creates a much smaller list of suitable books for a
particular user, say 500. But even 500 books is way too many to recommend
to a user. Subsequent, more expensive, phases of a recommendation system
(such as **scoring** and **re-ranking**) whittle
down those 500 to a much smaller, more useful set of recommendations.

## candidate sampling

A training-time optimization in which a probability is calculated for all the
positive labels, using, for example, **softmax**,
but only for a random
sample of negative labels. For example, if we have an example labeled
*beagle* and *dog* candidate sampling computes the predicted probabilities
and corresponding loss terms for the *beagle* and *dog* class outputs
in addition to a random subset of the remaining classes
(*cat*, *lollipop*, *fence*). The idea is that the
**negative classes** can learn from less frequent
negative reinforcement as long as
**positive classes** always get proper positive
reinforcement, and this is indeed observed empirically. The motivation for
candidate sampling is a computational efficiency win from not computing
predictions for all negatives.

## categorical data

**Features** having a discrete set of possible values. For example,
consider a categorical feature named `house style`

, which has a discrete set of
three possible values: `Tudor, ranch, colonial`

. By representing `house style`

as categorical data, the model can learn the separate impacts of `Tudor`

,
`ranch`

, and `colonial`

on house price.

Sometimes, values in the discrete set are mutually exclusive, and only one
value can be applied to a given example. For example, a `car maker`

categorical feature would probably permit only a single value (`Toyota`

)
per example. Other times, more than one value may be applicable. A single
car could be painted more than one different color, so a `car color`

categorical feature would likely permit a single example to have multiple
values (for example, `red`

and `white`

).

Categorical features are sometimes called
**discrete features**.

Contrast with **numerical data**.

## causal language model

Synonym for **unidirectional language model**.

See **bidirectional language model** to
contrast different directional approaches in language modeling.

## centroid

The center of a cluster as determined by a **k-means** or
**k-median** algorithm. For instance, if k is 3,
then the k-means or k-median algorithm finds 3 centroids.

## centroid-based clustering

A category of **clustering** algorithms that organizes data
into nonhierarchical clusters. **k-means** is the most widely
used centroid-based clustering algorithm.

Contrast with **hierarchical clustering**
algorithms.

## checkpoint

Data that captures the state of the variables of a model at a particular
time. Checkpoints enable exporting model **weights**, as well
as performing training across multiple sessions. Checkpoints also enable
training to continue past errors (for example, job preemption). Note that
the **graph** itself is not included in a checkpoint.

## class

One of a set of enumerated target values for a label. For example, in a
**binary classification** model that detects
spam, the two classes are *spam* and *not spam*. In a
**multi-class classification** model that
identifies dog breeds, the classes would be *poodle*, *beagle*, *pug*, and so
on.

## classification model

A type of machine learning model for distinguishing among two or more
discrete classes. For example, a natural language processing classification
model could determine whether an input sentence was in French, Spanish,
or Italian. Compare with **regression model**.

## classification threshold

A scalar-value criterion that is applied to a model's predicted score in order
to separate the **positive class** from the **negative
class**. Used when mapping
**logistic regression** results to
**binary classification**. For example, consider
a logistic regression model that determines the probability of a given email
message being spam. If the classification threshold is 0.9, then logistic
regression values above 0.9 are classified as *spam* and those below
0.9 are classified as *not spam*.

## class-imbalanced dataset

A **binary classification** problem in which the
**labels** for the two classes have significantly different
frequencies. For example, a disease dataset in which 0.0001 of examples
have positive labels and 0.9999 have negative labels is a class-imbalanced
problem, but a football game predictor in which 0.51 of examples label one
team winning and 0.49 label the other team winning is *not* a
class-imbalanced problem.

## clipping

A technique for handling **outliers**. Specifically, reducing
feature values that are greater than a set maximum value down to that maximum
value. Also, increasing feature values that are less than a specific minimum
value up to that minimum value.

For example, suppose that only a few feature values fall outside the range 40–60. In this case, you could do the following:

- Clip all values over 60 to be exactly 60.
- Clip all values under 40 to be exactly 40.

In addition to bringing *input values* within a designated range, clipping
can also used to force *gradient values* within a designated range during
training.

## Cloud TPU

A specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform.

## clustering

Grouping related **examples**, particularly during
**unsupervised learning**. Once all the
examples are grouped, a human can optionally supply meaning to each cluster.

Many clustering algorithms exist. For example, the **k-means**
algorithm clusters examples based on their proximity to a
**centroid**, as in the following diagram:

A human researcher could then review the clusters and, for example, label cluster 1 as "dwarf trees" and cluster 2 as "full-size trees."

As another example, consider a clustering algorithm based on an example's distance from a center point, illustrated as follows:

## co-adaptation

When **neurons** predict patterns in training data by relying
almost exclusively on outputs of specific other neurons instead of relying on
the network's behavior as a whole. When the patterns that cause co-adaption
are not present in validation data, then co-adaptation causes overfitting.
**Dropout regularization** reduces co-adaptation
because dropout ensures neurons cannot rely solely on specific other neurons.

## collaborative filtering

Making **predictions** about the interests of one user
based on the interests of many other users. Collaborative filtering
is often used in **recommendation systems**.

## confirmation bias

The tendency to search for, interpret, favor, and recall information in a
way that confirms one's preexisting beliefs or hypotheses.
Machine learning developers may inadvertently collect or label
data in ways that influence an outcome supporting their existing
beliefs. Confirmation bias is a form of **implicit bias**.

**Experimenter's bias** is a form of confirmation bias in which
an experimenter continues training models until a preexisting
hypothesis is confirmed.

## confusion matrix

An NxN table that summarizes how successful a
**classification model's** predictions were; that is,
the correlation between the label and the model's classification. One axis of
a confusion matrix is the **label** that the model predicted, and the
other axis is the actual label. N represents the number of
**classes**. In a **binary classification**
problem, N=2. For example, here is a sample confusion matrix for a
binary classification problem:

Tumor (predicted) | Non-Tumor (predicted) | |
---|---|---|

Tumor (actual) | 18 | 1 |

Non-Tumor (actual) | 6 | 452 |

The preceding confusion matrix shows that of the 19 samples that actually had
tumors, the model correctly classified 18 as having tumors
(18 **true positives**), and incorrectly classified 1 as
not having a tumor (1 **false negative**). Similarly, of
458 samples that actually did not have tumors, 452 were correctly classified
(452 **true negatives**) and 6 were
incorrectly classified (6 **false positives**).

The confusion matrix for a **multi-class classification**
problem can help you determine mistake patterns. For example, a
confusion matrix could reveal that a model trained to recognize handwritten
digits tends to mistakenly predict 9 instead of 4, or 1 instead of 7.

Confusion matrices contain sufficient information to calculate a
variety of performance metrics, including **precision**
and **recall**.

## continuous feature

A floating-point feature with an infinite range of possible values.
Contrast with **discrete feature**.

## convenience sampling

Using a dataset not gathered scientifically in order to run quick experiments. Later on, it's essential to switch to a scientifically gathered dataset.

## convergence

Informally, often refers to a state reached during **training**
in which training **loss** and **validation** loss
change very little or not at all
with each iteration after a certain number of iterations. In other words, a
model reaches convergence when additional training on the current data will
not improve the model. In **deep learning**, loss values
sometimes stay constant or nearly so for many iterations before finally
descending, temporarily producing a false sense of convergence.

See also **early stopping**.

See also Boyd and Vandenberghe, Convex Optimization.

## convex function

A function in which the region above the graph of the function is a
**convex set**. The prototypical convex function is
shaped something like the letter **U**. For example, the following
are all convex functions:

By contrast, the following function is not convex. Notice how the region above the graph is not a convex set:

A **strictly convex function** has exactly one local minimum point, which
is also the global minimum point. The classic U-shaped functions are
strictly convex functions. However, some convex functions
(for example, straight lines) are not U-shaped.

A lot of the common **loss functions**, including the
following, are convex functions:

Many variations of **gradient descent** are
guaranteed to find a point close to the minimum of a
strictly convex function. Similarly, many variations of
**stochastic gradient descent** have a high probability
(though, not a guarantee) of finding a point close to the minimum of a
strictly convex function.

The sum of two convex functions (for example,
L_{2} loss + L_{1} regularization) is a convex function.

**Deep models** are never convex functions.
Remarkably, algorithms designed for
**convex optimization** tend to find
reasonably good solutions on deep networks anyway, even though
those solutions are not guaranteed to be a global minimum.

## convex optimization

The process of using mathematical techniques such as
**gradient descent** to find
the minimum of a **convex function**.
A great deal of research in machine learning has focused on formulating various
problems as convex optimization problems and in solving those problems more
efficiently.

For complete details, see Boyd and Vandenberghe, Convex Optimization.

## convex set

A subset of Euclidean space such that a line drawn between any two points in the subset remains completely within the subset. For instance, the following two shapes are convex sets:

By contrast, the following two shapes are not convex sets:

## convolution

In mathematics, casually speaking, a mixture of two functions. In machine
learning, a convolution mixes the convolutional filter and the input matrix
in order to train **weights**.

The term "convolution" in machine learning is often a shorthand way of
referring to either **convolutional operation**
or **convolutional layer**.

Without convolutions, a machine learning algorithm would have to learn
a separate weight for every cell in a large **tensor**. For example,
a machine learning algorithm training on 2K x 2K images would be forced to
find 4M separate weights. Thanks to convolutions, a machine learning
algorithm only has to find weights for every cell in the
**convolutional filter**, dramatically reducing
the memory needed to train the model. When the convolutional filter is
applied, it is simply replicated across cells such that each is multiplied
by the filter.

## convolutional filter

One of the two actors in a
**convolutional operation**. (The other actor
is a slice of an input matrix.) A convolutional filter is a matrix having
the same **rank** as the input matrix, but a smaller shape.
For example, given a 28x28 input matrix, the filter could be any 2D matrix
smaller than 28x28.

In photographic manipulation, all the cells in a convolutional filter are
typically set to a constant pattern of ones and zeroes. In machine learning,
convolutional filters are typically seeded with random numbers and then the
network **trains** the ideal values.

## convolutional layer

A layer of a **deep neural network** in which a
**convolutional filter** passes along an input
matrix. For example, consider the following 3x3
**convolutional filter**:

The following animation shows a convolutional layer consisting of 9 convolutional operations involving the 5x5 input matrix. Notice that each convolutional operation works on a different 3x3 slice of the input matrix. The resulting 3x3 matrix (on the right) consists of the results of the 9 convolutional operations:

## convolutional neural network

A **neural network** in which at least one layer is a
**convolutional layer**. A typical convolutional
neural network consists of some combination of the following layers:

Convolutional neural networks have had great success in certain kinds of problems, such as image recognition.

## convolutional operation

The following two-step mathematical operation:

- Element-wise multiplication of the
**convolutional filter**and a slice of an input matrix. (The slice of the input matrix has the same rank and size as the convolutional filter.) - Summation of all the values in the resulting product matrix.

For example, consider the following 5x5 input matrix:

Now imagine the following 2x2 convolutional filter:

Each convolutional operation involves a single 2x2 slice of the input matrix. For instance, suppose we use the 2x2 slice at the top-left of the input matrix. So, the convolution operation on this slice looks as follows:

A **convolutional layer** consists of a
series of convolutional operations, each acting on a different slice
of the input matrix.

## cost

Synonym for **loss**.

## co-training

A **semi-supervised learning** approach
particularly useful when all of the following conditions are true:

- The ratio of
**unlabeled examples**to**labeled examples**in the dataset is high. - This is a classification problem (
**binary**or**multi-class**). - The dataset contains two different sets of predictive features that are independent of each other and complementary.

Co-training essentially amplifies independent signals into a stronger signal.
For instance, consider a **classification model** that
categorizes individual used cars as either *Good* or *Bad*. One set of
predictive features might focus on aggregate characteristics such as the year,
make, and model of the car; another set of predictive features might focus on
the previous owner's driving record and the car's maintenance history.

The seminal paper on co-training is Combining Labeled and Unlabeled Data with Co-Training by Blum and Mitchell.

## counterfactual fairness

A**fairness metric**that checks whether a classifier produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more

**sensitive attributes**. Evaluating a classifier for counterfactual fairness is one method for surfacing potential sources of bias in a model.

See "When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness" for a more detailed discussion of counterfactual fairness.

## coverage bias

See **selection bias**.

## crash blossom

A sentence or phrase with an ambiguous meaning.
Crash blossoms present a significant problem in **natural
language understanding**.
For example, the headline *Red Tape Holds Up Skyscraper* is a
crash blossom because an NLU model could interpret the headline literally or
figuratively.

## critic

Synonym for **Deep Q-Network**.

## cross-entropy

A generalization of **Log Loss** to
**multi-class classification problems**. Cross-entropy
quantifies the difference between two probability distributions. See also
**perplexity**.

## cross-validation

A mechanism for estimating how well a model will generalize to new data by
testing the model against one or more non-overlapping data subsets withheld
from the **training set**.

## D

## data analysis

Obtaining an understanding of data by considering samples, measurement, and visualization. Data analysis can be particularly useful when a dataset is first received, before one builds the first model. It is also crucial in understanding experiments and debugging problems with the system.

## data augmentation

Artificially boosting the range and number of **training** examples
by transforming existing examples to create additional examples. For example,
suppose images are one of your features, but your dataset doesn't contain
enough image examples for the model to learn useful associations. Ideally,
you'd add enough **labeled** images to your dataset to enable your
model to train properly. If that's not possible, data augmentation can rotate,
stretch, and reflect each image to produce many variants of the original
picture, possibly yielding enough labeled data to enable excellent training.

## DataFrame

A popular datatype for representing datasets in **pandas**. A
DataFrame is analogous to a table. Each column of the DataFrame has a name (a
header), and each row is identified by a number.

## data parallelism

A way of scaling training or inference that replicates an entire model onto
multiple devices and then passes a subset of the input data to each device.
Data parallelism can enable training and inference on very large
**batch sizes**; however, data parallelism requires that the
model be small enough to fit on all devices.

See also **model parallelism**.

## data set or dataset

A collection of **examples**.

## Dataset API (tf.data)

A high-level **TensorFlow** API for reading data and
transforming it into a form that a machine learning algorithm requires.
A `tf.data.Dataset`

object represents a sequence of elements, in which
each element contains one or more **Tensors**. A `tf.data.Iterator`

object provides access to the elements of a `Dataset`

.

For details about the Dataset API, see Importing Data in the TensorFlow Programmer's Guide.

## decision boundary

The separator between classes learned by a model in a
**binary class** or
**multi-class classification problems**. For example,
in the following image representing a binary classification problem,
the decision boundary is the frontier between the orange class and
the blue class:

## decision threshold

Synonym for **classification threshold**.

## decision tree

A model represented as a sequence of branching statements. For example, the following over-simplified decision tree branches a few times to predict the price of a house (in thousands of USD). According to this decision tree, a house larger than 160 square meters, having more than three bedrooms, and built less than 10 years ago would have a predicted price of 510 thousand USD.

Machine learning can generate deep decision trees.

## deep model

A type of **neural network** containing multiple
**hidden layers**.

Contrast with **wide model**.

## decoder

In general, any ML system that converts from a processed, dense, or internal representation to a more raw, sparse, or external representation.

Decoders are often a component of a larger model, where they are frequently
paired with an **encoder**.

In **sequence-to-sequence tasks**, a decoder
starts with the internal state generated by the encoder to predict the next
sequence.

Refer to **Transformer** for the definition of a decoder within
the Transformer architecture.

## deep neural network

Synonym for **deep model**.

## Deep Q-Network (DQN)

In **Q-learning**, a deep **neural network**
that predicts **Q-functions**.

**Critic** is a synonym for Deep Q-Network.

## demographic parity

A **fairness metric** that is satisfied if
the results of a model's classification are not dependent on a
given **sensitive attribute**.

For example, if both Lilliputians and Brobdingnagians apply to Glubbdubdrib University, demographic parity is achieved if the percentage of Lilliputians admitted is the same as the percentage of Brobdingnagians admitted, irrespective of whether one group is on average more qualified than the other.

Contrast with **equalized odds** and
**equality of opportunity**, which permit
classification results in aggregate to depend on sensitive attributes,
but do not permit classification results for certain specified ground-truth
labels to depend on sensitive attributes. See
"Attacking
discrimination with smarter machine learning" for a visualization
exploring the tradeoffs when optimizing for demographic parity.

## denoising

A common approach to **self-supervised learning**
in which:

**Noise**is artificially added to the dataset.- The model tries to remove the noise.

Denoising enables learning from **unlabeled examples**.
The original dataset serves as the target or label and the noisy data as the
input.

Some **masked language models** use denoising
as follows:

- Noise is artificially added to an unlabeled sentence by masking some of the tokens.
- The model tries to predict the original tokens.

## dense feature

A **feature** in which most values are non-zero, typically
a **Tensor** of floating-point values. Contrast with
**sparse feature**.

## dense layer

Synonym for **fully connected layer**.

## depth

The number of **layers** (including any
**embedding** layers) in a **neural network**
that learn weights. For example, a neural network with 5
**hidden layers** and 1 output layer has a depth of 6.

## depthwise separable convolutional neural network (sepCNN)

A **convolutional neural network**
architecture based on
Inception,
but where Inception modules are replaced with depthwise separable
convolutions. Also known as Xception.

A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n ✕ n ✕ 1), and then second, a pointwise convolution, with length and width of 1 (1 ✕ 1 ✕ n).

To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions.

## device

A category of hardware that can run a TensorFlow session, including
CPUs, GPUs, and **TPUs**.

## dimension reduction

Decreasing the number of dimensions used to represent a particular feature in
a feature vector, typically by converting to an **embedding**.

## dimensions

Overloaded term having any of the following definitions:

The number of levels of coordinates in a

**Tensor**. For example:- A scalar has zero dimensions; for example,
`["Hello"]`

. - A vector has one dimension; for example,
`[3, 5, 7, 11]`

. - A matrix has two dimensions; for example,
`[[2, 4, 18], [5, 7, 14]]`

.

You can uniquely specify a particular cell in a one-dimensional vector with one coordinate; you need two coordinates to uniquely specify a particular cell in a two-dimensional matrix.

- A scalar has zero dimensions; for example,
The number of entries in a

**feature vector**.The number of elements in an

**embedding**layer.

## discrete feature

A **feature** with a finite set of possible values. For example,
a feature whose values may only be *animal*, *vegetable*, or *mineral* is a
discrete (or categorical) feature. Contrast with
**continuous feature**.

## discriminative model

A **model** that predicts labels from a set of one or more
features. More formally, discriminative models define the conditional
probability of an output given the features and weights; that is:

p(output | features, weights)

For example, a model that predicts whether an email is spam from features and weights is a discriminative model.

The vast majority of supervised learning models, including classification and regression models, are discriminative models.

Contrast with **generative model**.

## discriminator

A system that determines whether examples are real or fake.

The subsystem within a **generative adversarial
network** that determines whether
the examples created by the **generator** are real or fake.

## disparate impact

Making decisions about people that impact different population subgroups disproportionately. This usually refers to situations where an algorithmic decision-making process harms or benefits some subgroups more than others.

For example, suppose an algorithm that determines a Lilliputian's eligibility for a miniature-home loan is more likely to classify them as “ineligible” if their mailing address contains a certain postal code. If Big-Endian Lilliputians are more likely to have mailing addresses with this postal code than Little-Endian Lilliputians, then this algorithm may result in disparate impact.

Contrast with **disparate treatment**,
which focuses on disparities that result when subgroup characteristics
are explicit inputs to an algorithmic decision-making process.

## disparate treatment

Factoring subjects' **sensitive attributes**
into an algorithmic decision-making process such that different subgroups
of people are treated differently.

For example, consider an algorithm that determines Lilliputians’ eligibility for a miniature-home loan based on the data they provide in their loan application. If the algorithm uses a Lilliputian’s affiliation as Big-Endian or Little-Endian as an input, it is enacting disparate treatment along that dimension.

Contrast with **disparate impact**, which focuses
on disparities in the societal impacts of algorithmic decisions on subgroups,
irrespective of whether those subgroups are inputs to the model.

## divisive clustering

## downsampling

Overloaded term that can mean either of the following:

- Reducing the amount of information in a feature in order to train a model more efficiently. For example, before training an image recognition model, downsampling high-resolution images to a lower-resolution format.
- Training on a disproportionately low percentage of over-represented class
examples in order to improve model training on under-represented classes.
For example, in a
**class-imbalanced dataset**, models tend to learn a lot about the**majority class**and not enough about the**minority class**. Downsampling helps balance the amount of training on the majority and minority classes.

## DQN

Abbreviation for **Deep Q-Network**.

## dropout regularization

A form of **regularization** useful in training
**neural networks**. Dropout regularization works by
removing a random selection of a fixed number of the units in a network
layer for a single gradient step. The more units dropped out, the stronger
the regularization. This is analogous to training the network to emulate
an exponentially large ensemble of smaller networks. For full details, see
Dropout: A Simple Way to Prevent Neural Networks from
Overfitting.

## dynamic model

A **model** that is trained online in a continuously
updating fashion. That is, data is continuously entering the model.

## E

## eager execution

A TensorFlow programming environment in which **operations**
run immediately. By contrast, operations called in
**graph execution** don't run until they are explicitly
evaluated. Eager execution is an
imperative interface, much
like the code in most programming languages. Eager execution programs are
generally far easier to debug than graph execution programs.

## early stopping

A method for **regularization** that involves ending
model training *before* training loss finishes decreasing. In early
stopping, you end model training when the loss on a
**validation dataset** starts to increase, that is, when
**generalization** performance worsens.

## earth mover's distance (EMD)

A measure of the relative similarity between two documents. The lower the value, the more similar the documents.

## embeddings

A categorical feature represented as a continuous-valued feature. Typically, an embedding is a translation of a high-dimensional vector into a low-dimensional space. For example, you can represent the words in an English sentence in either of the following two ways:

- As a million-element (high-dimensional)
**sparse vector**in which all elements are integers. Each cell in the vector represents a separate English word; the value in a cell represents the number of times that word appears in a sentence. Since a single English sentence is unlikely to contain more than 50 words, nearly every cell in the vector will contain a 0. The few cells that aren't 0 will contain a low integer (usually 1) representing the number of times that word appeared in the sentence. - As a several-hundred-element (low-dimensional)
**dense vector**in which each element holds a floating-point value between 0 and 1. This is an embedding.

In TensorFlow, embeddings are trained by **backpropagating**
**loss** just like any other parameter in a
**neural network**.

## embedding space

The d-dimensional vector space that features from a higher-dimensional vector space are mapped to. Ideally, the embedding space contains a structure that yields meaningful mathematical results; for example, in an ideal embedding space, addition and subtraction of embeddings can solve word analogy tasks.

The dot product of two embeddings is a measure of their similarity.

## empirical risk minimization (ERM)

Choosing the function that minimizes loss on the training set. Contrast
with **structural risk minimization**.

## encoder

In general, any ML system that converts from a raw, sparse, or external representation into a more processed, denser, or more internal representation.

Encoders are often a component of a larger model, where they are frequently
paired with a **decoder**. Some **Transformers**
pair encoders with decoders, though other Transformers use only the encoder
or only the decoder.

Some systems use the encoder's output as the input to a classification or regression network.

In **sequence-to-sequence tasks**, an encoder
takes an input sequence and returns an internal state (a vector). Then, the
**decoder** uses that internal state to predict the next sequence.

Refer to **Transformer** for the definition of an encoder in
the Transformer architecture.

## ensemble

A merger of the predictions of multiple **models**. You can create an
ensemble via one or more of the following:

- different initializations
- different
**hyperparameters** - different overall structure

Deep and wide models are a kind of ensemble.

## environment

In reinforcement learning, the world that contains the **agent**
and allows the agent to observe that world's **state**. For example,
the represented world can be a game like chess, or a physical world like a
maze. When the agent applies an **action** to the environment,
then the environment transitions between states.

## episode

In reinforcement learning, each of the repeated attempts by the
**agent** to learn an **environment**.

## epoch

A full training pass over the entire dataset such that each example has been
seen once. Thus, an epoch represents `N`

/**batch size** training
**iterations**, where `N`

is the total number of examples.

## epsilon greedy policy

In reinforcement learning, a **policy** that either follows a
**random policy** with epsilon probability or a
**greedy policy** otherwise. For example, if epsilon is
0.9, then the policy follows a random policy 90% of the time and a greedy
policy 10% of the time.

Over successive episodes, the algorithm reduces epsilon’s value in order to shift from following a random policy to following a greedy policy. By shifting the policy, the agent first randomly explores the environment and then greedily exploits the results of random exploration.

## equality of opportunity

A**fairness metric**that checks whether, for a preferred

**label**(one that confers an advantage or benefit to a person) and a given

**attribute**, a classifier predicts that preferred label equally well for all values of that attribute. In other words, equality of opportunity measures whether the people who should qualify for an opportunity are equally likely to do so regardless of their group membership.

For example, suppose Glubbdubdrib University admits both Lilliputians and Brobdingnagians to a rigorous mathematics program. Lilliputians’ secondary schools offer a robust curriculum of math classes, and the vast majority of students are qualified for the university program. Brobdingnagians’ secondary schools don’t offer math classes at all, and as a result, far fewer of their students are qualified. Equality of opportunity is satisfied for the preferred label of "admitted" with respect to nationality (Lilliputian or Brobdingnagian) if qualified students are equally likely to be admitted irrespective of whether they're a Lilliputian or a Brobdingnagian.

For example, let's say 100 Lilliputians and 100 Brobdingnagians apply to Glubbdubdrib University, and admissions decisions are made as follows:

**Table 1.** Lilliputian applicants (90% are qualified)

Qualified | Unqualified | |
---|---|---|

Admitted | 45 | 3 |

Rejected | 45 | 7 |

Total | 90 | 10 |

Percentage of qualified students admitted: 45/90 = 50% Percentage of unqualified students rejected: 7/10 = 70% Total percentage of Lilliputian students admitted: (45+3)/100 = 48% |

**Table 2.** Brobdingnagian applicants (10% are qualified):

Qualified | Unqualified | |
---|---|---|

Admitted | 5 | 9 |

Rejected | 5 | 81 |

Total | 10 | 90 |

Percentage of qualified students admitted: 5/10 = 50% Percentage of unqualified students rejected: 81/90 = 90% Total percentage of Brobdingnagian students admitted: (5+9)/100 = 14% |

The preceding examples satisfy equality of opportunity for acceptance of qualified students because qualified Lilliputians and Brobdingnagians both have a 50% chance of being admitted.

See "Equality of Opportunity in Supervised Learning" for a more detailed discussion of equality of opportunity. Also see "Attacking discrimination with smarter machine learning" for a visualization exploring the tradeoffs when optimizing for equality of opportunity.

## equalized odds

A**fairness metric**that checks if, for any particular label and attribute, a classifier predicts that label equally well for all values of that attribute.

For example, suppose Glubbdubdrib University admits both Lilliputians and Brobdingnagians to a rigorous mathematics program. Lilliputians' secondary schools offer a robust curriculum of math classes, and the vast majority of students are qualified for the university program. Brobdingnagians' secondary schools don’t offer math classes at all, and as a result, far fewer of their students are qualified. Equalized odds is satisfied provided that no matter whether an applicant is a Lilliputian or a Brobdingnagian, if they are qualified, they are equally as likely to get admitted to the program, and if they are not qualified, they are equally as likely to get rejected.

Let’s say 100 Lilliputians and 100 Brobdingnagians apply to Glubbdubdrib University, and admissions decisions are made as follows:

**Table 3.** Lilliputian applicants (90% are qualified)

Qualified | Unqualified | |
---|---|---|

Admitted | 45 | 2 |

Rejected | 45 | 8 |

Total | 90 | 10 |

Percentage of qualified students admitted: 45/90 = 50% Percentage of unqualified students rejected: 8/10 = 80% Total percentage of Lilliputian students admitted: (45+2)/100 = 47% |

**Table 4.** Brobdingnagian applicants (10% are qualified):

Qualified | Unqualified | |
---|---|---|

Admitted | 5 | 18 |

Rejected | 5 | 72 |

Total | 10 | 90 |

Percentage of qualified students admitted: 5/10 = 50% Percentage of unqualified students rejected: 72/90 = 80% Total percentage of Brobdingnagian students admitted: (5+18)/100 = 23% |

Equalized odds is satisfied because qualified Lilliputian and Brobdingnagian students both have a 50% chance of being admitted, and unqualified Lilliputian and Brobdingnagian have an 80% chance of being rejected.

Equalized odds is formally defined in "Equality of Opportunity in Supervised Learning" as follows: "predictor Ŷ satisfies equalized odds with respect to protected attribute A and outcome Y if Ŷ and A are independent, conditional on Y."

## Estimator

A deprecated TensorFlow API. Use tf.keras instead of Estimators.

## example

One row of a dataset. An example contains one or more **features**
and possibly a **label**. See also
**labeled example** and
**unlabeled example**.

## experience replay

In reinforcement learning, a **DQN** technique used to
reduce temporal correlations in training data. The **agent**
stores state transitions in a **replay buffer**, and then
samples transitions from the replay buffer to create training data.

## experimenter's bias

See **confirmation bias**.

## exploding gradient problem

The tendency for **gradients** in a
**deep neural networks** (especially
**recurrent neural networks**) to become
surprisingly steep (high). Steep gradients result in very large updates
to the weights of each node in a deep neural network.

Models suffering from the exploding gradient problem become difficult
or impossible to train. **Gradient clipping**
can mitigate this problem.

Compare to **vanishing gradient problem**.

## F

## fairness constraint

Applying a constraint to an algorithm to ensure one or more definitions of fairness are satisfied. Examples of fairness constraints include:**Post-processing**your model's output.- Altering the
**loss function**to incorporate a penalty for violating a**fairness metric**. - Directly adding a mathematical constraint to an optimization problem.

## fairness metric

A mathematical definition of “fairness” that is measurable. Some commonly used fairness metrics include:

Many fairness metrics are mutually exclusive; see
**incompatibility of fairness metrics**.

## false negative (FN)

An example in which the model mistakenly predicted the
**negative class**. For example, the model
inferred that a particular email message was not spam
(the negative class), but that email message actually was spam.

## false negative rate

The proportion of actual positive examples for which the negative class is predicted. False negative rate is calculated as follows:

## false positive (FP)

An example in which the model mistakenly predicted the
**positive class**. For example, the model inferred
that a particular email message was spam (the positive class), but that
email message was actually not spam.

## false positive rate (FPR)

The x-axis in an **ROC curve**. The false positive rate is defined
as follows:

## feature

An input variable used in making **predictions**.

## feature cross

A **synthetic feature** formed by crossing (taking a
Cartesian
product of) individual binary features obtained from
**categorical data** or from
**continuous features** via **bucketing**.
Feature crosses help represent nonlinear relationships.

## feature engineering

The process of determining which **features** might be useful
in training a model, and then converting raw data from log files and other
sources into said features. In TensorFlow, feature engineering often means
converting raw log file entries to **tf.Example**
protocol buffers. See also
tf.Transform.

Feature engineering is sometimes called **feature extraction**.

## feature extraction

Overloaded term having either of the following definitions:

- Retrieving intermediate feature representations calculated by an
**unsupervised**or pretrained model (for example,**hidden layer**values in a**neural network**) for use in another model as input. - Synonym for
**feature engineering**.

## feature set

The group of **features** your machine learning model trains on.
For example, postal code, property size, and property condition might
comprise a simple feature set for a model that predicts housing prices.

## feature spec

Describes the information required to extract **features** data
from the **tf.Example** protocol buffer. Because the
tf.Example protocol buffer is just a container for data, you must specify
the following:

- the data to extract (that is, the keys for the features)
- the data type (for example, float or int)
- The length (fixed or variable)

## feature vector

The list of feature values representing an **example**
passed into a model.

## federated learning

A distributed machine learning approach that **trains**
machine learning **models** using decentralized
**examples** residing on devices such as smartphones.
In federated learning, a subset of devices downloads the current model
from a central coordinating server. The devices use the examples stored
on the devices to make improvements to the model. The devices then upload
the model improvements (but not the training examples) to the coordinating
server, where they are aggregated with other updates to yield an improved
global model. After the aggregation, the model updates computed by devices
are no longer needed, and can be discarded.

Since the training examples are never uploaded, federated learning follows the privacy principles of focused data collection and data minimization.

For more information about federated learning, see this tutorial.

## feedback loop

In machine learning, a situation in which a model's predictions influence the training data for the same model or another model. For example, a model that recommends movies will influence the movies that people see, which will then influence subsequent movie recommendation models.

## feedforward neural network (FFN)

A neural network without cyclic or recursive connections. For example,
traditional **deep neural networks** are
feedforward neural networks. Contrast with **recurrent neural
networks**, which are cyclic.

## few-shot learning

A machine learning approach, often used for object classification, designed to learn effective classifiers from only a small number of training examples.

See also **one-shot learning**.

## fine tuning

Perform a secondary optimization to adjust the parameters of an already
trained **model** to fit a new problem. Fine tuning often
refers to refitting the weights of a trained
**unsupervised** model to a
**supervised** model.

## forget gate

The portion of a **Long Short-Term Memory**
cell that regulates the flow of information through the cell.
Forget gates maintain context by deciding which information to discard
from the cell state.

## full softmax

See **softmax**. Contrast with
**candidate sampling**.

## fully connected layer

A **hidden layer** in which each **node** is
connected to *every* node in the subsequent hidden layer.

A fully connected layer is also known as a **dense layer**.

## G

## GAN

Abbreviation for **generative adversarial
network**.

## generalization

Refers to your model's ability to make correct predictions on new, previously unseen data as opposed to the data used to train the model.

## generalization curve

A **loss curve** showing both the
**training set** and the
**validation set**.
A generalization curve can help you detect possible
**overfitting**. For example, the following
generalization curve suggests overfitting because loss for
the validation set ultimately becomes significantly higher
than for the training set.

## generalized linear model

A generalization of **least squares regression**
models, which are based on
Gaussian
noise, to other
types of models based on other types of noise, such as
Poisson noise
or
categorical noise. Examples of generalized linear models include:

**logistic regression**- multi-class regression
- least squares regression

The parameters of a generalized linear model can be found through
**convex optimization**.

Generalized linear models exhibit the following properties:

- The average prediction of the optimal least squares regression model is equal to the average label on the training data.
- The average probability predicted by the optimal logistic regression model is equal to the average label on the training data.

The power of a generalized linear model is limited by its features. Unlike a deep model, a generalized linear model cannot "learn new features."

## generative adversarial network (GAN)

A system to create new data in which a **generator** creates
data and a **discriminator** determines whether that
created data is valid or invalid.

## generative model

Practically speaking, a model that does either of the following:

- Creates (generates) new examples from the training dataset.
For example, a generative model could create poetry after training
on a dataset of poems. The
**generator**part of a**generative adversarial network**falls into this category. - Determines the probability that a new example comes from the training set, or was created from the same mechanism that created the training set. For example, after training on a dataset consisting of English sentences, a generative model could determine the probability that new input is a valid English sentence.

A generative model can theoretically discern the distribution of examples or particular features in a dataset. That is:

p(examples)

Unsupervised learning models are generative.

Contrast with **discriminative models**.

## generator

The subsystem within a **generative adversarial
network**
that creates new **examples**.

Contrast with **discriminative model**.

## GPT (Generative Pre-trained Transformer)

A family of **Transformer**-based
**large language models** developed by
OpenAI.

GPT variants can apply to multiple **modalities**, including:

- image generation (for example, ImageGPT)
- text-to-image generation (for example, DALL-E).

## gradient

The vector of **partial derivatives** with respect to
all of the independent variables. In machine learning, the gradient is
the vector of partial derivatives of the model function. The gradient points
in the direction of steepest ascent.

## gradient clipping

A commonly used mechanism to mitigate the
**exploding gradient problem** by artificially
limiting (clipping) the maximum value of gradients when using
**gradient descent** to train a model.

## gradient descent

A technique to minimize **loss** by computing the gradients of
loss with respect to the model's parameters, conditioned on training data.
Informally, gradient descent iteratively adjusts parameters, gradually
finding the best combination of **weights** and bias to
minimize loss.

## graph

In TensorFlow, a computation specification. Nodes in the graph
represent operations. Edges are directed and represent passing the result
of an operation (a **Tensor**) as an
operand to another operation. Use
**TensorBoard** to visualize a graph.

## graph execution

A TensorFlow programming environment in which the program first constructs
a **graph** and then executes all or part of that graph. Graph
execution is the default execution mode in TensorFlow 1.x.

Contrast with **eager execution**.

## greedy policy

In reinforcement learning, a **policy** that always chooses the
action with the highest expected **return**.

## ground truth

The correct answer. Reality. Since reality is often subjective,
expert **raters** typically are the proxy for ground truth.

## group attribution bias

Assuming that what is true for an individual is also true for everyone
in that group. The effects of group attribution bias can be exacerbated
if a **convenience sampling**
is used for data collection. In a non-representative sample, attributions
may be made that do not reflect reality.

See also **out-group homogeneity bias**
and **in-group bias**.

## H

## hashing

In machine learning, a mechanism for bucketing
**categorical data**, particularly when the number
of categories is large, but the number of categories actually appearing
in the dataset is comparatively small.

For example, Earth is home to about 60,000 tree species. You could represent each of the 60,000 tree species in 60,000 separate categorical buckets. Alternatively, if only 200 of those tree species actually appear in a dataset, you could use hashing to divide tree species into perhaps 500 buckets.

A single bucket could contain multiple tree species. For example, hashing
could place *baobab* and *red maple*—two genetically dissimilar
species—into the same bucket. Regardless, hashing is still a good way to
map large categorical sets into the desired number of buckets. Hashing turns a
categorical feature having a large number of possible values into a much
smaller number of values by grouping values in a
deterministic way.

## heuristic

A simple and quickly implemented solution to a problem. For example, "With a heuristic, we achieved 86% accuracy. When we switched to a deep neural network, accuracy went up to 98%."

## hidden layer

A synthetic layer in a **neural network** between the
**input layer** (that is, the features) and the
**output layer** (the prediction). Hidden layers typically
contain an **activation function** (such as
**ReLU**) for training. A **deep neural
network** contains more than one
hidden layer.

## hierarchical clustering

A category of **clustering** algorithms that create a tree
of clusters. Hierarchical clustering is well-suited to hierarchical data,
such as botanical taxonomies. There are two types of hierarchical
clustering algorithms:

**Agglomerative clustering**first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree.**Divisive clustering**first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree.

Contrast with **centroid-based clustering**.

## hinge loss

A family of **loss** functions for
**classification** designed to find the
**decision boundary** as distant as possible
from each training example,
thus maximizing the margin between examples and the boundary.
**KSVMs** use hinge loss (or a related function, such as
squared hinge loss). For binary classification, the hinge loss function
is defined as follows:

where *y* is the true label, either -1 or +1, and *y'* is the raw output
of the classifier model:

Consequently, a plot of hinge loss vs. (y * y') looks as follows:

## holdout data

**Examples** intentionally not used ("held out") during training.
The **validation dataset** and
**test dataset** are examples of holdout data. Holdout data
helps evaluate your model's ability to generalize to data other than the
data it was trained on. The loss on the holdout set provides a better
estimate of the loss on an unseen dataset than does the loss on the
training set.

## hyperparameter

The "knobs" that you
tweak during successive runs of training a model. For example,
**learning rate** is a hyperparameter.

Contrast with **parameter**.

## hyperplane

A boundary that separates a space into two subspaces. For example, a line is a
hyperplane in two dimensions and a plane is a hyperplane in three dimensions.
More typically in machine learning, a hyperplane is the boundary separating a
high-dimensional space. **Kernel Support Vector Machines** use
hyperplanes to separate positive classes from negative classes, often in a very
high-dimensional space.

## I

## i.i.d.

Abbreviation for **independently and identically distributed**.

## image recognition

A process that classifies object(s), pattern(s), or concept(s) in an image.
Image recognition is also known as **image classification**.

For more information, see ML Practicum: Image Classification.

## imbalanced dataset

Synonym for **class-imbalanced dataset**.

## implicit bias

Automatically making an association or assumption based on one’s mental models and memories. Implicit bias can affect the following:

- How data is collected and classified.
- How machine learning systems are designed and developed.

For example, when building a classifier to identify wedding photos, an engineer may use the presence of a white dress in a photo as a feature. However, white dresses have been customary only during certain eras and in certain cultures.

See also **confirmation bias**.

## incompatibility of fairness metrics

The idea that some notions of fairness are mutually incompatible and
cannot be satisfied simultaneously. As a result, there is no single
universal **metric** for quantifying fairness
that can be applied to all ML problems.

While this may seem discouraging, incompatibility of fairness metrics doesn’t imply that fairness efforts are fruitless. Instead, it suggests that fairness must be defined contextually for a given ML problem, with the goal of preventing harms specific to its use cases.

See "On the (im)possibility of fairness" for a more detailed discussion of this topic.

## independently and identically distributed (i.i.d)

Data drawn from a distribution that doesn't change, and where each value drawn doesn't depend on values that have been drawn previously. An i.i.d. is the ideal gas of machine learning—a useful mathematical construct but almost never exactly found in the real world. For example, the distribution of visitors to a web page may be i.i.d. over a brief window of time; that is, the distribution doesn't change during that brief window and one person's visit is generally independent of another's visit. However, if you expand that window of time, seasonal differences in the web page's visitors may appear.

## individual fairness

A fairness metric that checks whether similar individuals are classified similarly. For example, Brobdingnagian Academy might want to satisfy individual fairness by ensuring that two students with identical grades and standardized test scores are equally likely to gain admission.

Note that individual fairness relies entirely on how you define "similarity" (in this case, grades and test scores), and you can run the risk of introducing new fairness problems if your similarity metric misses important information (such as the rigor of a student’s curriculum).

See "Fairness Through Awareness" for a more detailed discussion of individual fairness.

## inference

In machine learning, often refers to the process of making predictions by
applying the trained model to **unlabeled examples**.
In statistics, inference refers to the process of fitting the parameters
of a distribution conditioned on some observed data. (See the
Wikipedia article on statistical inference.)

## in-group bias

Showing partiality to one's own group or own characteristics. If testers or raters consist of the machine learning developer's friends, family, or colleagues, then in-group bias may invalidate product testing or the dataset.

In-group bias is a form of
**group attribution bias**.
See also **out-group homogeneity bias**.

## input layer

The first layer (the one that receives the input data) in
a **neural network**.

## instance

Synonym for **example**.

## interpretability

The ability to explain or to present an ML model's reasoning in understandable terms to a human.

## inter-rater agreement

A measurement of how often human raters agree when doing a task.
If raters disagree, the task instructions may need to be improved.
Also sometimes called **inter-annotator agreement** or
**inter-rater reliability**. See also
Cohen's
kappa,
which is one of the most popular inter-rater agreement measurements.

## intersection over union (IoU)

The intersection of two sets divided by their union. In machine-learning
image-detection tasks, IoU is used to measure the accuracy of the model’s
predicted **bounding box** with respect to the
**ground-truth** bounding box. In this case, the IoU for the
two boxes is the ratio between the overlapping area and the total area, and
its value ranges from 0 (no overlap of predicted bounding box and ground-truth
bounding box) to 1 (predicted bounding box and ground-truth bounding box have
the exact same coordinates).

For example, in the image below:

- The predicted bounding box (the coordinates delimiting where the model predicts the night table in the painting is located) is outlined in purple.
- The ground-truth bounding box (the coordinates delimiting where the night table in the painting is actually located) is outlined in green.

Here, the intersection of the bounding boxes for prediction and ground truth (below left) is 1, and the union of the bounding boxes for prediction and ground truth (below right) is 7, so the IoU is \(\frac{1}{7}\).

## IoU

Abbreviation for **intersection over union**.

## item matrix

In **recommendation systems**, a
matrix of **embeddings** generated by
**matrix factorization**
that holds latent signals about each **item**.
Each row of the item matrix holds the value of a single latent
feature for all items.
For example, consider a movie recommendation system. Each column
in the item matrix represents a single movie. The latent signals
might represent genres, or might be harder-to-interpret
signals that involve complex interactions among genre, stars,
movie age, or other factors.

The item matrix has the same number of columns as the target matrix that is being factorized. For example, given a movie recommendation system that evaluates 10,000 movie titles, the item matrix will have 10,000 columns.

## items

In a **recommendation system**, the entities that
a system recommends. For example, videos are the items that a video store
recommends, while books are the items that a bookstore recommends.

## iteration

A single update of a model's weights during training. An iteration
consists of computing the gradients of the parameters with respect to the
loss on a single **batch** of data.

## K

## Keras

A popular Python machine learning API. Keras runs on several deep learning frameworks, including TensorFlow, where it is made available as tf.keras.

## keypoints

The coordinates of particular features in an image. For example, for an
**image recognition** model that distinguishes
flower species, keypoints might be the center of each petal, the stem,
the stamen, and so on.

## Kernel Support Vector Machines (KSVMs)

A classification algorithm that seeks to maximize the margin between
**positive** and
**negative classes** by mapping input data vectors
to a higher dimensional space. For example, consider a classification
problem in which the input dataset
has a hundred features. To maximize the margin between
positive and negative classes, a KSVM could internally map those features into
a million-dimension space. KSVMs uses a loss function called
**hinge loss**.

## k-means

A popular **clustering** algorithm that groups examples
in unsupervised learning. The k-means algorithm basically does the following:

- Iteratively determines the best k center points (known
as
**centroids**). - Assigns each example to the closest centroid. Those examples nearest the same centroid belong to the same group.

The k-means algorithm picks centroid locations to minimize the cumulative
*square* of the distances from each example to its closest centroid.

For example, consider the following plot of dog height to dog width:

If k=3, the k-means algorithm will determine three centroids. Each example is assigned to its closest centroid, yielding three groups:

Imagine that a manufacturer wants to determine the ideal sizes for small,
medium, and large sweaters for dogs. The three centroids identify the mean
height and mean width of each dog in that cluster. So, the manufacturer
should probably base sweater sizes on those three centroids. Note that
the centroid of a cluster is typically *not* an example in the cluster.

The preceding illustrations shows k-means for examples with only two features (height and width). Note that k-means can group examples across many features.

## k-median

A clustering algorithm closely related to **k-means**. The
practical difference between the two is as follows:

- In k-means, centroids are determined by minimizing the sum of the
*squares*of the distance between a centroid candidate and each of its examples. - In k-median, centroids are determined by minimizing the sum of the distance between a centroid candidate and each of its examples.

Note that the definitions of distance are also different:

- k-means relies on the Euclidean distance from the centroid to an example. (In two dimensions, the Euclidean distance means using the Pythagorean theorem to calculate the hypotenuse.) For example, the k-means distance between (2,2) and (5,-2) would be:

- k-median relies on the Manhattan distance from the centroid to an example. This distance is the sum of the absolute deltas in each dimension. For example, the k-median distance between (2,2) and (5,-2) would be:

## L

## L_{1} loss

**Loss** function based on the absolute value of the difference
between the values that a model is predicting and the actual values of
the **labels**. L_{1} loss is less sensitive to outliers
than **L _{2} loss**.

## L_{1} regularization

A type of **regularization** that penalizes weights
in proportion to the sum of the absolute values of the weights. In models
relying on **sparse features**, L_{1}
regularization helps drive the weights of irrelevant or barely relevant
features to exactly 0, which removes those features from the model.
Contrast with **L _{2} regularization**.

## L_{2} loss

See **squared loss**.

## L_{2} regularization

A type of **regularization** that penalizes
**weights** in proportion to the sum of the *squares* of the weights.
L_{2} regularization helps drive **outlier** weights (those
with high positive or low negative values) closer to 0 but not quite to 0.
(Contrast with **L1 regularization**.)
L_{2} regularization always improves generalization in linear models.

## label

In supervised learning, the "answer" or "result" portion of an
**example**. Each example in a labeled dataset consists of one or
more features and a label. For instance, in a housing dataset, the features
might include the number of bedrooms, the number of bathrooms, and the age
of the house, while the label might be the house's price.
In a spam detection dataset, the features might include the subject line, the
sender, and the email message itself, while the label would probably be either
"spam" or "not spam."

## labeled example

An example that contains **features** and a
**label**. In supervised training, models learn from labeled
examples.

## LaMDA (Language Model for Dialogue Applications)

A **Transformer**-based
**large language model** developed by Google trained on
a large dialogue dataset that can generate realistic conversational responses.

LaMDA: our breakthrough conversation technology provides an overview.

## lambda

Synonym for **regularization rate**.

(This is an overloaded term. Here we're focusing on the term's
definition within **regularization**.)

## landmarks

Synonym for **keypoints**.

## language model

A **model** that estimates the probability of a **token**
or sequence of tokens occurring in a longer sequence of tokens.

## large language model

An informal term with no strict definition that usually means a
**language model** that has a high number of
**parameters**.
Some large language models contain over 100 billion parameters.

## layer

A set of **neurons** in a
**neural network** that process a set of input
features, or the output of those neurons.

Also, an abstraction in TensorFlow. Layers are Python
functions that take **Tensors** and configuration options
as input and produce other tensors as output.

## Layers API (tf.layers)

A TensorFlow API for constructing a **deep** neural network
as a composition of layers. The Layers API enables you to build different
types of **layers**, such as:

`tf.layers.Dense`

for a**fully-connected layer**.`tf.layers.Conv2D`

for a convolutional layer.

The Layers API follows the **Keras** layers API conventions.
That is, aside from a different prefix, all functions in the Layers API
have the same names and signatures as their counterparts in the Keras
layers API.

## learning rate

A scalar used to train a model via gradient descent. During each iteration,
the **gradient descent** algorithm multiplies the
learning rate by the gradient. The resulting product is called the
**gradient step**.

Learning rate is a key **hyperparameter**.

## least squares regression

A linear regression model trained by minimizing
**L _{2} Loss**.

## linear model

A **model** that assigns one **weight** per
**feature** to make **predictions**.
(Linear models also incorporate a **bias**.) By contrast,
the relationship of weights to features in **deep models**
is not one-to-one.

A linear model uses the following formula:

where:

- \(y'\) is the raw prediction. (In certain kinds of linear models, this raw prediction will be further modified. For example, see logistic regression.)
- \(b\) is the
**bias**. - \(w\) is a
**weight**, so \(w_1\) is the weight of the first feature, \(w_2\) is the weight of the second feature, and so on. - \(x\) is a
**feature**, so \(x_1\) is the value of the first feature, \(x_2\) is the value of the second feature, and so on.

For example, suppose a linear model for three features learns the following bias and weights:

- \(b\) = 7
- \(w_1\) = -2.5
- \(w_2\) = -1.2
- \(w_3\) = 1.4

Therefore, given three features (\(x_1\), \(x_2\), and \(x_3\)), the linear model uses the following equation to generate each prediction:

Suppose a particular example contains the following values:

- \(x_1\) = 4
- \(x_2\) = -10
- \(x_3\) = 5

Plugging those values into the formula yields a prediction for this example:

Linear models tend to be easier to analyze and train than deep models. However,
deep models can model complex relationships *between* features.

**Linear regression** and
**logistic regression** are two types of linear models.
Linear models include not only models that use the linear equation but also a
broader set of models that use the linear equation as part of the formula.
For example, logistic regression post-processes the raw
prediction (\(y'\)) to calculate the prediction.

## linear regression

Using the raw output (\(y'\)) of a **linear model** as the
actual prediction in a **regression model**. The goal of
a regression problem is to make a real-valued prediction. For example, if
the raw output (\(y'\)) of a linear model is 8.37, then the prediction is
8.37.

Contrast linear regression with **logistic regression**.
Also, contrast regression with **classification**.

## logistic regression

A **classification model** that uses a
**sigmoid function** to convert
a **linear model's** raw prediction (\(y'\)) into a
value between 0 and 1. You can interpret the value between 0 and 1 in either
of the following two ways:

- As a probability that the example belongs to the
**positive class**in a binary classification problem. - As a value to be compared against a
**classification threshold**. If the value is equal to or above the classification threshold, the system classifies the example as the positive class. Conversely, if the value is below the given threshold, the system classifies the example as the**negative class**. For example, suppose the classification threshold is 0.82:- Imagine an example that produces a raw prediction (\(y'\)) of 2.6. The sigmoid of 2.6 is 0.93. Since 0.93 is greater than 0.82, the system classifies this example as the positive class.
- Imagine a different example that produces a raw prediction of 1.3. The sigmoid of 1.3 is 0.79. Since 0.79 is less than 0.82, the system classifies that example as the negative class.

Although logistic regression is often used in
**binary classification** problems, logistic
regression can also be used in
**multi-class classification** problems
(where it becomes called **multi-class logistic regression** or
**multinomial regression**).

## logits

The vector of raw (non-normalized) predictions that a classification
model generates, which is ordinarily then passed to a normalization function.
If the model is solving a **multi-class classification**
problem, logits typically become an input to the
**softmax** function.
The softmax function then generates a vector of (normalized)
probabilities with one value for each possible class.

In addition, logits sometimes refer to the element-wise inverse of the
**sigmoid function**. For more information, see
tf.nn.sigmoid_cross_entropy_with_logits.

## Log Loss

The **loss** function used in binary
**logistic regression**.

## log-odds

The logarithm of the odds of some event.

If the event refers to a binary probability, then **odds** refers to
the ratio of the probability of success (p) to the probability of
failure (1-p). For example, suppose that a given event has a 90%
probability of success and a 10% probability of failure. In this case,
odds is calculated as follows:

The log-odds is simply the logarithm of the odds. By convention, "logarithm" refers to natural logarithm, but logarithm could actually be any base greater than 1. Sticking to convention, the log-odds of our example is therefore:

The log-odds are the inverse of the **sigmoid function**.

## Long Short-Term Memory (LSTM)

A type of cell in a
**recurrent neural network** used to process
sequences of data in applications such as handwriting recognition, machine
translation, and image captioning. LSTMs address the
**vanishing gradient problem** that occurs when
training RNNs due to long data sequences by maintaining history in an
internal memory state based on new input and context from previous cells
in the RNN.

## loss

A measure of how far a model's **predictions** are from its
**label**. Or, to phrase it more pessimistically, a measure of
how bad the model is. To determine this value, a model must define a loss
function. For example, linear regression models typically use
**mean squared error** for a loss function,
while logistic regression models use **Log Loss**.

## loss curve

A graph of **loss** as a function of training
**iterations**. For example:

The loss curve can help you determine when your model is
**converging**, **overfitting**,
or **underfitting**.

## loss surface

A graph of weight(s) vs. loss. **Gradient descent** aims
to find the weight(s) for which the loss surface is at a local minimum.

## LSTM

Abbreviation for **Long Short-Term Memory**.

## M

## machine learning

A program or system that builds (trains) a predictive model from input data. The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems.

## majority class

The more common label in a
**class-imbalanced dataset**. For example,
given a dataset containing 99% non-spam labels and 1% spam labels, the
non-spam labels are the majority class.

## Markov decision process (MDP)

A graph representing the decision-making model where decisions
(or **actions**) are taken to navigate a sequence of
**states** under the assumption that the
**Markov property** holds. In reinforcement learning,
these transitions between states return a numerical **reward**.

## Markov property

A property of certain **environments**, where state
transitions are entirely determined by information implicit in the
current **state** and the agent’s **action**.

## masked language model

A **language model** that predicts the probability of
candidate tokens to fill in blanks in a sequence. For instance, a
masked language model can calculate probabilities for candidate word(s)
to replace the underline in the following sentence:

The ____ in the hat came back.

The literature typically uses the string "MASK" instead of an underline. For example:

The "MASK" in the hat came back.

Most modern masked language models are **bidirectional**.

## matplotlib

An open-source Python 2D plotting library. matplotlib helps you visualize different aspects of machine learning.

## matrix factorization

In math, a mechanism for finding the matrices whose dot product approximates a target matrix.

In **recommendation systems**, the target matrix
often holds users' ratings on **items**. For example, the target
matrix for a movie recommendation system might look something like the
following, where the positive integers are user ratings and 0
means that the user didn't rate the movie:

Casablanca | The Philadelphia Story | Black Panther | Wonder Woman | Pulp Fiction | |
---|---|---|---|---|---|

User 1 | 5.0 | 3.0 | 0.0 | 2.0 | 0.0 |

User 2 | 4.0 | 0.0 | 0.0 | 1.0 | 5.0 |

User 3 | 3.0 | 1.0 | 4.0 | 5.0 | 0.0 |

The movie recommendation system aims to predict user ratings for
unrated movies. For example, will User 1 like *Black Panther*?

One approach for recommendation systems is to use matrix factorization to generate the following two matrices:

- A
**user matrix**, shaped as the number of users X the number of embedding dimensions. - An
**item matrix**, shaped as the number of embedding dimensions X the number of items.

For example, using matrix factorization on our three users and five items could yield the following user matrix and item matrix:

User Matrix Item Matrix 1.1 2.3 0.9 0.2 1.4 2.0 1.2 0.6 2.0 1.7 1.2 1.2 -0.1 2.1 2.5 0.5

The dot product of the user matrix and item matrix yields a recommendation
matrix that contains not only the original user ratings but also predictions
for the movies that each user hasn't seen.
For example, consider User 1's rating of *Casablanca*, which was 5.0. The dot
product corresponding to that cell in the recommendation matrix should
hopefully be around 5.0, and it is:

(1.1 * 0.9) + (2.3 * 1.7) = 4.9

More importantly, will User 1 like *Black Panther*? Taking the dot product
corresponding to the first row and the third column yields a predicted
rating of 4.3:

(1.1 * 1.4) + (2.3 * 1.2) = 4.3

Matrix factorization typically yields a user matrix and item matrix that, together, are significantly more compact than the target matrix.

## Mean Absolute Error (MAE)

An error metric calculated by taking an average of absolute errors. In the context of evaluating a model’s accuracy, MAE is the average absolute difference between the expected and predicted values across all training examples. Specifically, for $n$ examples, for each value $y$ and its prediction $\hat{y}$, MAE is defined as follows:

\[\text{MAE} = \frac{1}{n}\sum_{i=0}^n | y_i - \hat{y}_i |\]

## Mean Squared Error (MSE)

The average squared loss per example. MSE is calculated by dividing the
**squared loss** by the number of
**examples**. The values that
**TensorFlow Playground** displays for
"Training loss" and "Test loss" are MSE.

## metric

A number that you care about. May or may not be directly optimized in a
machine-learning system. A metric that your system tries to optimize is
called an **objective**.

## meta-learning

A subset of machine learning that discovers or improves a learning algorithm. A meta-learning system can also aim to train a model to quickly learn a new task from a small amount of data or from experience gained in previous tasks. Meta-learning algorithms generally try to achieve the following:

- Improve/learn hand-engineered features (such as an initializer or an optimizer).
- Be more data-efficient and compute-efficient.
- Improve generalization.

Meta-learning is related to **few-shot learning**.

## Metrics API (tf.metrics)

A TensorFlow API for evaluating models. For example, `tf.metrics.accuracy`

determines how often a model's predictions match labels.

## mini-batch

A small, randomly selected subset of the entire batch of
**examples** run together in a single iteration of training
or inference. The **batch size** of a mini-batch is usually
between 10 and 1,000. It is much more efficient to calculate the loss on a
mini-batch than on the full training data.

## mini-batch stochastic gradient descent

A **gradient descent** algorithm that uses
**mini-batches**. In other words, mini-batch stochastic
gradient descent estimates the gradient based on a small subset of the
training data. Regular **stochastic gradient descent** uses a
mini-batch of size 1.

## minimax loss

A loss function for
**generative adversarial networks**,
based on the **cross-entropy** between the distribution
of generated data and real data.

Minimax loss is used in the first paper to describe generative adversarial networks.

## minority class

The less common label in a
**class-imbalanced dataset**. For example,
given a dataset containing 99% non-spam labels and 1% spam labels, the
spam labels are the minority class.

## ML

Abbreviation for **machine learning**.

## MNIST

A public-domain dataset compiled by LeCun, Cortes, and Burges containing 60,000 images, each image showing how a human manually wrote a particular digit from 0–9. Each image is stored as a 28x28 array of integers, where each integer is a grayscale value between 0 and 255, inclusive.

MNIST is a canonical dataset for machine learning, often used to test new machine learning approaches. For details, see The MNIST Database of Handwritten Digits.

## modality

A high-level data category. For example, numbers, text, images, video, and audio are five different modalities.

## model

The representation of what a machine learning system has learned from the training data. Within TensorFlow, model is an overloaded term, which can have either of the following two related meanings:

- The
**TensorFlow**graph that expresses the structure of how a prediction will be computed. - The particular weights and biases of that TensorFlow graph, which are
determined by
**training**.

## model capacity

The complexity of problems that a model can learn. The more complex the problems that a model can learn, the higher the model’s capacity. A model’s capacity typically increases with the number of model parameters. For a formal definition of classifier capacity, see VC dimension.

## model parallelism

A way of scaling training or inference that puts different parts of one model on different devices. Model parallelism enables models that are too big to fit on a single device.

See also **data parallelism**.

## model training

The process of determining the best **model**.

## Momentum

A sophisticated gradient descent algorithm in which a learning step depends not only on the derivative in the current step, but also on the derivatives of the step(s) that immediately preceded it. Momentum involves computing an exponentially weighted moving average of the gradients over time, analogous to momentum in physics. Momentum sometimes prevents learning from getting stuck in local minima.

## multi-class classification

Classification problems that distinguish among more than two classes. For
example, there are approximately 128 species of maple trees, so a model
that categorized maple tree species would be multi-class. Conversely, a
model that divided emails into only two categories (*spam* and *not spam*)
would be a **binary classification model**.

## multi-class logistic regression

Using **logistic regression** in
**multi-class classification** problems.

## multi-head self-attention

An extension of **self-attention** that applies the
self-attention mechanism multiple times for each position in the input sequence.

**Transformers** introduced multi-head self-attention.

## multimodal model

A model whose inputs and/or outputs include more than one
**modality**. For example, consider a model that takes both an
image and a text caption (two modalities) as **features**, and
outputs a score indicating how appropriate the text caption is for the image.
So, this model's inputs are multimodal and the output is unimodal.

## multinomial classification

Synonym for **multi-class classification**.

## multinomial regression

Synonym for **multi-class logistic regression**.

## N

## NaN trap

When one number in your model becomes a NaN during training, which causes many or all other numbers in your model to eventually become a NaN.

NaN is an abbreviation for "Not a Number."

## natural language understanding

Determining a user's intentions based on what the user typed or said. For example, a search engine uses natural language understanding to determine what the user is searching for based on what the user typed or said.

## negative class

In **binary classification**, one class is
termed positive and the other is termed negative. The positive class is
the thing we're looking for and the negative class is the other possibility.
For example, the negative class in a medical test might be "not tumor."
The negative class in an email classifier might be "not spam."
See also **positive class**.

## neural network

A model that, taking inspiration from the brain, is composed of layers
(at least one of which is **hidden**) consisting of
simple connected units or **neurons** followed by nonlinearities.

## neuron

A node in a **neural network**, typically taking in
multiple input values and generating one output value. The neuron calculates
the output value by applying an
**activation function** (nonlinear transformation)
to a weighted sum of input values.

## N-gram

An ordered sequence of N words. For example, *truly madly* is a 2-gram. Because
order is relevant, *madly truly* is a different 2-gram than *truly madly*.

N | Name(s) for this kind of N-gram | Examples |
---|---|---|

2 | bigram or 2-gram | to go, go to, eat lunch, eat dinner |

3 | trigram or 3-gram | ate too much, three blind mice, the bell tolls |

4 | 4-gram | walk in the park, dust in the wind, the boy ate lentils |

Many **natural language understanding**
models rely on N-grams to predict the next word that the user will type
or say. For example, suppose a user typed *three blind*.
An NLU model based on trigrams would likely predict that the
user will next type *mice*.

Contrast N-grams with **bag of words**, which are
unordered sets of words.

## NLU

Abbreviation for **natural language
understanding**.

## node (neural network)

A **neuron** in a **hidden layer**.

## node (TensorFlow graph)

An operation in a TensorFlow **graph**.

## noise

Broadly speaking, anything that obscures the signal in a dataset. Noise can be introduced into data in a variety of ways. For example:

- Human raters make mistakes in labeling.
- Humans and instruments mis-record or omit feature values.

## non-response bias

See **selection bias**.

## nonstationarity

A feature whose values change across one or more dimensions, usually time. For example, the number of swimsuits sold at a particular store demonstrates nonstationarity because that number varies with the season. As a second example, the quantity of a particular fruit harvested in a particular region typically shows sharp nonstationarity over time.

## normalization

The process of converting an actual range of values into a standard range of values, typically -1 to +1 or 0 to 1. For example, suppose the natural range of a certain feature is 800 to 6,000. Through subtraction and division, you can normalize those values into the range -1 to +1.

See also **scaling**.

## novelty detection

The process of determining whether a new (novel) example comes from the same
distribution as the **training set**. In other words, after
training on the training set, novelty detection determines whether a *new*
example (during inference or during additional training) is an
**outlier**.

Contrast with **outlier detection**.

## numerical data

**Features** represented as integers or real-valued numbers.
For example, in a real estate model, you would probably represent the size
of a house (in square feet or square meters) as numerical data. Representing
a feature as numerical data indicates that the feature's values have
a *mathematical* relationship to each other and possibly to the label.
For example, representing the size of a house as numerical data indicates
that a 200 square-meter house is twice as large as a 100 square-meter house.
Furthermore, the number of square meters in a house probably has some
mathematical relationship to the price of the house.

Not all integer data should be represented as numerical data. For example,
postal codes in some parts of the world are integers; however, integer postal
codes should not be represented as numerical data in models. That's because a
postal code of `20000`

is not twice (or half) as potent as a postal code of
10000. Furthermore, although different postal codes *do* correlate to different
real estate values, we can't assume that real estate values at postal code
20000 are twice as valuable as real estate values at postal code 10000.
Postal codes should be represented as **categorical data**
instead.

Numerical features are sometimes called
**continuous features**.

## NumPy

An
open-source math library
that provides efficient array operations in Python.
**pandas** is built on NumPy.

## O

## objective

A metric that your algorithm is trying to optimize.

## objective function

The mathematical formula or metric that a model aims to optimize.
For example, the objective function for
**linear regression** is usually
**squared loss**. Therefore, when training a
linear regression model, the goal is to minimize squared loss.

In some cases, the goal is to maximize the objective function. For example, if the objective function is accuracy, the goal is to maximize accuracy.

See also **loss**.

## offline inference

Generating a group of **predictions**, storing those
predictions, and then retrieving those predictions on demand. Contrast
with **online inference**.

## one-hot encoding

A sparse vector in which:

- One element is set to 1.
- All other elements are set to 0.

One-hot encoding is commonly used to represent strings or identifiers that have a finite set of possible values. For example, suppose a given botany dataset chronicles 15,000 different species, each denoted with a unique string identifier. As part of feature engineering, you'll probably encode those string identifiers as one-hot vectors in which the vector has a size of 15,000.

## one-shot learning

A machine learning approach, often used for object classification, designed to learn effective classifiers from a single training example.

See also **few-shot learning**.

## one-vs.-all

Given a classification problem with N possible solutions, a one-vs.-all
solution consists of N separate
**binary classifiers**—one binary classifier for
each possible outcome. For example, given a model that classifies examples
as animal, vegetable, or mineral, a one-vs.-all solution would provide the
following three separate binary classifiers:

- animal vs. not animal
- vegetable vs. not vegetable
- mineral vs. not mineral

## online inference

Generating **predictions** on demand. Contrast with
**offline inference**.

## Operation (op)

A node in the TensorFlow graph. In TensorFlow, any procedure that creates,
manipulates, or destroys a **Tensor** is an operation. For
example, a matrix multiply is an operation that takes two Tensors as
input and generates one Tensor as output.

## optimizer

A specific implementation of the **gradient descent**
algorithm. Popular optimizers include:

- [
**AdaGrad**], which stands for ADAptive GRADient descent. - Adam, which stands for ADAptive with Momentum.

## out-group homogeneity bias

The tendency to see out-group members as more alike than in-group members
when comparing attitudes, values, personality traits, and other
characteristics. **In-group** refers to people you interact with regularly;
**out-group** refers to people you do not interact with regularly. If you
create a dataset by asking people to provide attributes about
out-groups, those attributes may be less nuanced and more stereotyped
than attributes that participants list for people in their in-group.

For example, Lilliputians might describe the houses of other Lilliputians in great detail, citing small differences in architectural styles, windows, doors, and sizes. However, the same Lilliputians might simply declare that Brobdingnagians all live in identical houses.

Out-group homogeneity bias is a form of
**group attribution bias**.

See also **in-group bias**.

## outlier detection

The process of identifying **outliers** in a
**training set**.

Contrast with **novelty detection**.

## outliers

Values distant from most other values. In machine learning, any of the following are outliers:

**Weights**with high absolute values.- Predicted values relatively far away from the actual values.
- Input data whose values are more than roughly 3 standard deviations from the mean.

Outliers often cause problems in model training. **Clipping**
is one way of managing outliers.

## output layer

The "final" layer of a neural network. The layer containing the answer(s).

## overfitting

Creating a model that matches the **training data** so
closely that the model fails to make correct predictions on new data.

## oversampling

Reusing the **examples** of a **minority class**
in a **class-imbalanced dataset** in order to
create a more balanced **training set**.

For example, consider a **binary classification**
problem in which the ratio of the **majority class** to the
minority class is 5,000:1. If the dataset contains a million examples, then
the dataset contains only about 200 examples of the minority class, which might
be too few examples for effective training. To overcome this deficiency, you
might oversample (reuse) those 200 examples multiple times, possibly yielding
sufficient examples for useful training.

You need to be careful about over **overfitting** when
oversampling.

Contrast with **undersampling**.

## P

## pandas

A column-oriented data analysis API. Many machine learning frameworks, including TensorFlow, support pandas data structures as input. See the pandas documentation for details.

## parameter

A variable of a model that the machine learning system trains on its own.
For example, **weights** are parameters whose values the
machine learning system gradually learns through successive training
iterations. Contrast with **hyperparameter**.

## Parameter Server (PS)

A job that keeps track of a model's **parameters** in a
distributed setting.

## parameter update

The operation of adjusting a model's **parameters** during
training, typically within a single iteration of
**gradient descent**.

## partial derivative

A derivative in which all but one of the variables is considered a constant.
For example, the partial derivative of *f(x, y)* with respect to *x* is the
derivative of *f* considered as a function of *x* alone (that is, keeping *y*
constant). The partial derivative of *f* with respect to *x* focuses only on
how *x* is changing and ignores all other variables in the equation.

## participation bias

Synonym for non-response bias. See **selection bias**.

## partitioning strategy

The algorithm by which variables are divided across
**parameter servers**.

## perceptron

A system (either hardware or software) that takes in one or more input values,
runs a function on the weighted sum of the inputs, and computes a single
output value. In machine learning, the function is typically nonlinear, such as
**ReLU**, **sigmoid**, or tanh.
For example, the following perceptron relies on the sigmoid function to process
three input values:

In the following illustration, the perceptron takes three inputs, each of which is itself modified by a weight before entering the perceptron:

Perceptrons are the (**nodes**) in **deep neural
networks**. That is, a deep neural network consists of
multiple connected perceptrons, plus a
**backpropagation** algorithm to introduce feedback.

## performance

Overloaded term with the following meanings:

- The traditional meaning within software engineering. Namely: How fast (or efficiently) does this piece of software run?
- The meaning within machine learning. Here, performance answers the
following question: How correct is this
**model**? That is, how good are the model's predictions?

## perplexity

One measure of how well a **model** is accomplishing its task.
For example, suppose your task is to read the first few letters of a word
a user is typing on a smartphone keyboard, and to offer a list of possible
completion words. Perplexity, P, for this task is approximately the number
of guesses you need to offer in order for your list to contain the actual
word the user is trying to type.

Perplexity is related to **cross-entropy** as follows:

## pipeline

The infrastructure surrounding a machine learning algorithm. A pipeline includes gathering the data, putting the data into training data files, training one or more models, and exporting the models to production.

## pipelining

A form of **model parallelism** in which a model's
processing is divided into consecutive stages and each stage is executed
on a different device. While a stage is processing one batch, the preceding
stage can work on the next batch.

See also **staged training**.

## policy

In reinforcement learning, an **agent's** probabilistic mapping
from **states** to **actions**.

## pooling

Reducing a matrix (or matrices) created by an earlier
**convolutional layer** to a smaller matrix.
Pooling usually involves taking either the maximum or average value
across the pooled area. For example, suppose we have the
following 3x3 matrix:

A pooling operation, just like a convolutional operation, divides that
matrix into slices and then slides that convolutional operation by
**strides**. For example, suppose the pooling operation
divides the convolutional matrix into 2x2 slices with a 1x1 stride.
As the following diagram illustrates, four pooling operations take place.
Imagine that each pooling operation picks the maximum value of the
four in that slice:

Pooling helps enforce
**translational invariance** in the input matrix.

Pooling for vision applications is known more formally as **spatial pooling**.
Time-series applications usually refer to pooling as **temporal pooling**.
Less formally, pooling is often called **subsampling** or **downsampling**.

## positive class

In **binary classification**, the two possible
classes are labeled as positive and negative. The positive outcome is the
thing we're testing for. (Admittedly, we're simultaneously testing for
both outcomes, but play along.) For example, the positive class in a
medical test might be "tumor." The positive class in an email classifier
might be "spam."

Contrast with **negative class**.

## post-processing

Processing the output of a model*after*the model has been run. Post-processing can be used to enforce fairness constraints without modifying models themselves.

For example, one might apply post-processing to a binary classifier
by setting a classification threshold such that
**equality of opportunity** is maintained
for some attribute by checking that the **true positive rate**
is the same for all values of that attribute.

## PR AUC (area under the PR curve)

Area under the interpolated
**precision-recall curve**, obtained by plotting
(recall, precision) points for different values of the
**classification threshold**. Depending on how
it's calculated, PR AUC may be equivalent to the
**average precision** of the model.

## precision

A metric for **classification models**. Precision
identifies the frequency with which a model was correct when predicting the
**positive class**. That is:

## precision-recall curve

A curve of **precision** vs. **recall** at different
**classification thresholds**.

## prediction

A model's output when provided with an input **example**.

## prediction bias

A value indicating how far apart the average of
**predictions** is from the average of **labels**
in the dataset.

Not to be confused with the **bias term** in machine learning models
or with **bias in ethics and fairness**.

## predictive parity

A **fairness metric** that checks whether,
for a given classifier, the **precision** rates
are equivalent for subgroups under consideration.

For example, a model that predicts college acceptance would satisfy predictive parity for nationality if its precision rate is the same for Lilliputians and Brobdingnagians.

Predictive parity is sometime also called *predictive rate parity*.

See "Fairness Definitions Explained" (section 3.2.1) for a more detailed discussion of predictive parity.

## predictive rate parity

Another name for **predictive parity**.

## preprocessing

Processing data before it's used to train a model. Preprocessing could be as simple as removing words from an English text corpus that don't occur in the English dictionary, or could be as complex as re-expressing data points in a way that eliminates as many attributes that are correlated with**sensitive attributes**as possible. Preprocessing can help satisfy

**fairness constraints**.

## pre-trained model

Models or model components (such as **embeddings**) that have
been already been trained. Sometimes, you'll feed pre-trained embeddings
into a **neural network**. Other times, your model will
train the embeddings itself rather than rely on the pre-trained embeddings.

## prior belief

What you believe about the data before you begin training on it. For
example, **L _{2} regularization** relies on
a prior belief that

**weights**should be small and normally distributed around zero.

## probabilistic regression model

A **regression model** that uses not only the
**weights** for each **feature**, but also the
uncertainty of those weights. A probabilistic regression model generates
a prediction and the uncertainty of that prediction. For example, a
probabilistic regression model might yield a prediction of 325 with a
standard deviation of 12. For more information about probabilistic regression
models, see this Colab on
tensorflow.org.

## proxy (sensitive attributes)

An attribute used as a stand-in for a**sensitive attribute**. For example, an individual's postal code might be used as a proxy for their income, race, or ethnicity.

## proxy labels

Data used to approximate labels not directly available in a dataset.

For example, suppose you want *is it raining?* to be a Boolean label
for your dataset, but the dataset doesn't contain rain data. If
photographs are available, you might establish pictures of people
carrying umbrellas as a proxy label for *is it raining?* However,
proxy labels may distort results. For example, in some places, it
may be more common to carry umbrellas to protect against sun than
the rain.

## Q

## Q-function

In reinforcement learning, the function that predicts the expected
**return** from taking an **action** in a
**state** and then following a given **policy**.

Q-function is also known as **state-action value function**.

## Q-learning

In reinforcement learning, an algorithm that allows an **agent**
to learn the optimal **Q-function** of a
**Markov decision process** by applying the
**Bellman equation**. The Markov decision process models
an **environment**.

## quantile

Each bucket in **quantile bucketing**.

## quantile bucketing

Distributing a feature's values into **buckets** so that each
bucket contains the same (or almost the same) number of examples. For example,
the following figure divides 44 points into 4 buckets, each of which
contains 11 points. In order for each bucket in the figure to contain the
same number of points, some buckets span a different width of x-values.

## quantization

An algorithm that implements **quantile bucketing** on
a particular **feature** in a **dataset**.

## queue

A TensorFlow **Operation** that implements a queue data
structure. Typically
used in I/O.

## R

## random forest

An ensemble approach to finding the **decision tree** that
best fits the training data by creating many decision trees and then
determining the "average" one. The "random" part of the term refers to
building each of the decision trees from a random selection of features;
the "forest" refers to the set of decision trees.

## random policy

In reinforcement learning, a **policy** that chooses an
**action** at random.

## rank (ordinality)

The ordinal position of a class in a machine learning problem that categorizes classes from highest to lowest. For example, a behavior ranking system could rank a dog's rewards from highest (a steak) to lowest (wilted kale).

## rank (Tensor)

The number of dimensions in a **Tensor**. For instance,
a scalar has rank 0, a vector has rank 1, and a matrix has rank 2.

Not to be confused with **rank (ordinality)**.

## rater

A human who provides **labels** in **examples**.
Sometimes called an "annotator."

## recall

A metric for **classification models** that answers
the following question: Out of all the possible positive labels, how many
did the model correctly identify? That is:

\[\text{Recall} = \frac{\text{True Positives}} {\text{True Positives} + \text{False Negatives}} \]

## recommendation system

A system that selects for each user a relatively small set of desirable
**items** from a large corpus.
For example, a video recommendation system might recommend two videos
from a corpus of 100,000 videos, selecting *Casablanca* and
*The Philadelphia Story* for one user, and *Wonder Woman* and
*Black Panther* for another. A video recommendation system might
base its recommendations on factors such as:

- Movies that similar users have rated or watched.
- Genre, directors, actors, target demographic...

## Rectified Linear Unit (ReLU)

An **activation function** with the following rules:

- If input is negative or zero, output is 0.
- If input is positive, output is equal to input.

## recurrent neural network

A **neural network** that is intentionally run multiple
times, where parts of each run feed into the next run. Specifically,
hidden layers from the previous run provide part of the
input to the same hidden layer in the next run. Recurrent neural networks
are particularly useful for evaluating sequences, so that the hidden layers
can learn from previous runs of the neural network on earlier parts of
the sequence.

For example, the following figure shows a recurrent neural network that runs four times. Notice that the values learned in the hidden layers from the first run become part of the input to the same hidden layers in the second run. Similarly, the values learned in the hidden layer on the second run become part of the input to the same hidden layer in the third run. In this way, the recurrent neural network gradually trains and predicts the meaning of the entire sequence rather than just the meaning of individual words.

## regression model

A type of model that outputs continuous (typically, floating-point) values.
Compare with **classification models**, which
output discrete values, such as "day lily" or "tiger lily."

## regularization

The penalty on a model's complexity. Regularization helps prevent
**overfitting**. Different kinds of regularization include:

**L**_{1}regularization**L**_{2}regularization**dropout regularization****early stopping**(this is not a formal regularization method, but can effectively limit overfitting)

## regularization rate

A scalar value, represented as lambda, specifying the relative importance
of the regularization function. The following simplified **loss**
equation shows the regularization rate's influence:

Raising the regularization rate reduces **overfitting**
but may make the model less **accurate**.

## reinforcement learning (RL)

A family of algorithms that learn an optimal **policy**, whose goal
is to maximize **return** when interacting with
an **environment**.
For example, the ultimate reward of most games is victory.
Reinforcement learning systems can become expert at playing complex
games by evaluating sequences of previous game moves that ultimately
led to wins and sequences that ultimately led to losses.

## replay buffer

In **DQN**-like algorithms, the memory used by the agent
to store state transitions for use in
**experience replay**.

## reporting bias

The fact that the frequency with which people write about actions, outcomes, or properties is not a reflection of their real-world frequencies or the degree to which a property is characteristic of a class of individuals. Reporting bias can influence the composition of data that machine learning systems learn from.

For example, in books, the word *laughed* is more prevalent than
*breathed*. A machine learning model that estimates the relative frequency of
laughing and breathing from a book corpus would probably determine
that laughing is more common than breathing.

## representation

The process of mapping data to useful **features**.

## re-ranking

The final stage of a **recommendation system**,
during which scored items may be re-graded according to some other
(typically, non-ML) algorithm. Re-ranking evaluates the list of items
generated by the **scoring** phase, taking actions such as:

- Eliminating items that the user has already purchased.
- Boosting the score of fresher items.

## return

In reinforcement learning, given a certain policy and a certain state, the
return is the sum of all **rewards** that the **agent**
expects to receive when following the **policy** from the
**state** to the end of the **episode**. The agent
accounts for the delayed nature of expected rewards by discounting rewards
according to the state transitions required to obtain the reward.

Therefore, if the discount factor is \(\gamma\), and \(r_0, \ldots, r_{N}\) denote the rewards until the end of the episode, then the return calculation is as follows:

## reward

In reinforcement learning, the numerical result of taking an
**action** in a **state**, as defined by
the **environment**.

## ridge regularization

Synonym for **L _{2} regularization**. The term

**ridge regularization**is more frequently used in pure statistics contexts, whereas

**L**is used more often in machine learning.

_{2}regularization## RNN

Abbreviation for **recurrent neural networks**.

## ROC (receiver operating characteristic) Curve

A curve of **true positive rate** vs.
**false positive rate** at different
**classification thresholds**. See also
**AUC**.

## root directory

The directory you specify for hosting subdirectories of the TensorFlow checkpoint and events files of multiple models.

## Root Mean Squared Error (RMSE)

The square root of the **Mean Squared Error**.

## rotational invariance

In an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes. For example, the algorithm can still identify a tennis racket whether it is pointing up, sideways, or down. Note that rotational invariance is not always desirable; for example, an upside-down 9 should not be classified as a 9.

See also **translational invariance** and
**size invariance**.

## S

## sampling bias

See **selection bias**.

## SavedModel

The recommended format for saving and recovering TensorFlow models. SavedModel is a language-neutral, recoverable serialization format, which enables higher-level systems and tools to produce, consume, and transform TensorFlow models.

See the Saving and Restoring chapter in the TensorFlow Programmer's Guide for complete details.

## Saver

A TensorFlow object responsible for saving model checkpoints.

## scalar

A single number or a single string that can be represented as a
**tensor** of **rank** 0. For example, the following
lines of code each create one scalar in TensorFlow:

breed = tf.Variable("poodle", tf.string) temperature = tf.Variable(27, tf.int16) precision = tf.Variable(0.982375101275, tf.float64)

## scaling

A commonly used practice in **feature engineering**
to tame a feature's range of values to match the range of other features in
the dataset. For example, suppose that you want all floating-point features
in the dataset to have a range of 0 to 1. Given a particular feature's
range of 0 to 500, you could scale that feature by dividing each value
by 500.

See also **normalization**.

## scikit-learn

A popular open-source machine learning platform. See scikit-learn.org.

## scoring

The part of a **recommendation system** that
provides a value or ranking for each item produced by the
**candidate generation** phase.

## selection bias

Errors in conclusions drawn from sampled data due to a selection process that generates systematic differences between samples observed in the data and those not observed. The following forms of selection bias exist:

**coverage bias**: The population represented in the dataset does not match the population that the machine learning model is making predictions about.**sampling bias**: Data is not collected randomly from the target group.**non-response bias**(also called**participation bias**): Users from certain groups opt-out of surveys at different rates than users from other groups.

For example, suppose you are creating a machine learning model that predicts people's enjoyment of a movie. To collect training data, you hand out a survey to everyone in the front row of a theater showing the movie. Offhand, this may sound like a reasonable way to gather a dataset; however, this form of data collection may introduce the following forms of selection bias:

- coverage bias: By sampling from a population who chose to see the movie, your model's predictions may not generalize to people who did not already express that level of interest in the movie.
- sampling bias: Rather than randomly sampling from the intended population (all the people at the movie), you sampled only the people in the front row. It is possible that the people sitting in the front row were more interested in the movie than those in other rows.
- non-response bias: In general, people with strong opinions tend to respond to optional surveys more frequently than people with mild opinions. Since the movie survey is optional, the responses are more likely to form a bimodal distribution than a normal (bell-shaped) distribution.

## self-attention (also called self-attention layer)

A neural network layer that transforms a sequence of
**embeddings** (for instance, **token** embeddings)
into another sequence of embeddings. Each embedding in the output sequence is
constructed by integrating information from the elements of the input sequence
through an **attention** mechanism.

The **self** part of **self-attention** refers to the sequence attending to
itself rather than to some other context. Self-attention is one of the main
building blocks for **Transformers** and uses dictionary lookup
terminology, such as “query”, “key”, and “value”.

A self-attention layer starts with a sequence of input representations, one for each word. The input representation for a word can be a simple embedding. For each word in an input sequence, the network scores the relevance of the word to every element in the whole sequence of words. The relevance scores determine how much the word's final representation incorporates the representations of other words.

For example, consider the following sentence:

The animal didn't cross the street because it was too tired.

The following illustration (from
Transformer: A Novel Neural Network Architecture for Language
Understanding)
shows a self-attention layer's attention pattern for the pronoun **it**, with
the darkness of each line indicating how much each word contributes to the
representation:

The self-attention layer highlights words that are relevant to "it". In this
case, the attention layer has learned to highlight words that **it** might
refer to, assigning the highest weight to **animal**.

For a sequence of *n* **tokens**, self-attention transforms a sequence
of embeddings *n* separate times, once at each position in the sequence.

Refer also to **attention** and
**multi-head self-attention**.

## self-supervised learning

A family of techniques for converting an
**unsupervised machine learning** problem
into a **supervised machine learning** problem
by creating surrogate **labels** from
**unlabeled examples**.

Some **Transformer**-based models such as **BERT** use
self-supervised learning.

Self-supervised training is a
**semi-supervised learning** approach.

## self-training

A variant of **self-supervised learning** that is
particularly useful when all of the following conditions are true:

- The ratio of
**unlabeled examples**to**labeled examples**in the dataset is high. - This is a
**classification**problem.

Self-training works by iterating over the following two steps until the model stops improving:

- Use
**supervised machine learning**to train a model on the labeled examples. - Use the model created in Step 1 to generate predictions (labels) on the unlabeled examples, moving those in which there is high confidence into the labeled examples with the predicted label.

Notice that each iteration of Step 2 adds more labeled examples for Step 1 to train on.

## semi-supervised learning

Training a model on data where some of the training examples have labels but others don't. One technique for semi-supervised learning is to infer labels for the unlabeled examples, and then to train on the inferred labels to create a new model. Semi-supervised learning can be useful if labels are expensive to obtain but unlabeled examples are plentiful.

**Self-training** is one technique for semi-supervised
learning.

## sensitive attribute

A human attribute that may be given special consideration for legal, ethical, social, or personal reasons.## sentiment analysis

Using statistical or machine learning algorithms to determine a group's
overall attitude—positive or negative—toward a service, product,
organization, or topic. For example, using
**natural language understanding**,
an algorithm could perform sentiment analysis on the textual feedback
from a university course to determine the degree to which students
generally liked or disliked the course.

## sequence model

A model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos.

## sequence-to-sequence task

A task that converts an input sequence of **tokens** to an output
sequence of tokens. For example, two popular kinds of sequence-to-sequence
tasks are:

- Translators:
- Sample input sequence: "I love you."
- Sample output sequence: "Je t'aime."

- Question answering:
- Sample input sequence: "Do I need my car in New York City?"
- Sample output sequence: "No. Please keep your car at home."

## serving

A synonym for **inferring**.

## shape (Tensor)

The number of elements in each **dimension** of a
tensor. The shape is represented as a list of integers. For example,
the following two-dimensional tensor has a shape of [3,4]:

[[5, 7, 6, 4], [2, 9, 4, 8], [3, 6, 5, 1]]

TensorFlow uses row-major (C-style) format to represent the order of
dimensions, which is why the shape in TensorFlow is [3,4] rather than
[4,3]. In other words, in a two-dimensional TensorFlow Tensor, the shape
is [*number of rows*, *number of columns*].

## sigmoid function

A function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1. The sigmoid function has the following formula:

where \(\sigma\) in **logistic regression** problems
is simply:

In other words, the sigmoid function converts \(\sigma\) into a probability between 0 and 1.

In some **neural networks**, the sigmoid function acts as
the **activation function**.

## similarity measure

In **clustering** algorithms, the metric used to determine
how alike (how similar) any two examples are.

## size invariance

In an image classification problem, an algorithm's ability to successfully classify images even when the size of the image changes. For example, the algorithm can still identify a cat whether it consumes 2M pixels or 200K pixels. Note that even the best image classification algorithms still have practical limits on size invariance. For example, an algorithm (or human) is unlikely to correctly classify a cat image consuming only 20 pixels.

See also **translational invariance** and
**rotational invariance**.

## sketching

In **unsupervised machine learning**,
a category of algorithms that perform a preliminary similarity analysis
on examples. Sketching algorithms use a
locality-sensitive hash function
to identify points that are likely to be similar, and then group
them into buckets.

Sketching decreases the computation required for similarity calculations on large datasets. Instead of calculating similarity for every single pair of examples in the dataset, we calculate similarity only for each pair of points within each bucket.

## softmax

A function that provides probabilities for each possible class in a
**multi-class classification model**. The probabilities add up
to exactly 1.0. For example, softmax might determine that the probability of a
particular image being a dog at 0.9, a cat at 0.08, and a horse at 0.02.
(Also called **full softmax**.)

Contrast with **candidate sampling**.

## sparse feature

**Feature** vector whose values are predominately zero or empty.
For example, a vector containing a single 1 value and a million 0 values is
sparse. As another example, words in a search query could also be a
sparse feature—there are many possible words in a given language, but only a
few of them occur in a given query.

Contrast with **dense feature**.

## sparse representation

A **representation** of a tensor that only stores
nonzero elements.

For example, the English language consists of about a million words. Consider two ways to represent a count of the words used in one English sentence:

- A
**dense representation**of this sentence must set an integer for all one million cells, placing a 0 in most of them, and a low integer into a few of them. - A sparse representation of this sentence stores only those cells symbolizing a word actually in the sentence. So, if the sentence contained only 20 unique words, then the sparse representation for the sentence would store an integer in only 20 cells.

For example, consider two ways to represent the sentence, "Dogs wag tails." As the following tables show, the dense representation consumes about a million cells; the sparse representation consumes only 3 cells:

Cell Number | Word | Occurrence |
---|---|---|

0 | a | 0 |

1 | aardvark | 0 |

2 | aargh | 0 |

3 | aarti | 0 |

… 140,391 more words with an occurrence of 0 |
||

140395 | dogs | 1 |

… 633,062 words with an occurrence of 0 |
||

773458 | tails | 1 |

… 189,135 words with an occurrence of 0 |
||

962594 | wag | 1 |

… many more words with an occurrence of 0 |

Cell Number | Word | Occurrence |
---|---|---|

140395 | dogs | 1 |

773458 | tails | 1 |

962594 | wag | 1 |

## sparse vector

A vector whose values are mostly zeroes. See also **sparse
feature**.

## sparsity

The number of elements set to zero (or null) in a vector or matrix divided by the total number of entries in that vector or matrix. For example, consider a 10x10 matrix in which 98 cells contain zero. The calculation of sparsity is as follows:

**Feature sparsity** refers to the sparsity of a feature vector;
**model sparsity** refers to the sparsity of the model weights.

## spatial pooling

See **pooling**.

## squared hinge loss

The square of the **hinge loss**. Squared hinge loss penalizes
outliers more harshly than regular hinge loss.

## squared loss

The **loss** function used in
**linear regression**. (Also known as
**L _{2} Loss**.) This function calculates the squares of
the difference between a model's predicted value for a labeled

**example**and the actual value of the

**label**. Due to squaring, this loss function amplifies the influence of bad predictions. That is, squared loss reacts more strongly to outliers than

**L**.

_{1}loss## staged training

A tactic of training a model in a sequence of discrete stages. The goal can be either to speed up the training process, or to achieve better model quality.

An illustration of the progressive stacking approach is shown below:

- Stage 1 contains 3 hidden layers, stage 2 contains 6 hidden layers, and stage 3 contains 12 hidden layers.
- Stage 2 begins training with the weights learned in the 3 hidden layers of Stage 1. Stage 3 begins training with the weights learned in the 6 hidden layers of Stage 2.

See also **pipelining**.

## state

In reinforcement learning, the parameter values that describe the current
configuration of the environment, which the **agent** uses to
choose an **action**.

## state-action value function

Synonym for **Q-function**.

## static model

A model that is trained offline.

## stationarity

A property of data in a dataset, in which the data distribution stays constant across one or more dimensions. Most commonly, that dimension is time, meaning that data exhibiting stationarity doesn't change over time. For example, data that exhibits stationarity doesn't change from September to December.

## step

A forward and backward evaluation of one **batch**.

## step size

Synonym for **learning rate**.

## stochastic gradient descent (SGD)

A **gradient descent** algorithm in which the batch size
is one. In other words, SGD relies on a single example chosen uniformly at
random from a dataset to calculate an estimate of the gradient at each step.

## stride

In a convolutional operation or pooling, the delta in each dimension of the next series of input slices. For example, the following animation demonstrates a (1,1) stride during a convolutional operation. Therefore, the next input slice starts one position to the right of the previous input slice. When the operation reaches the right edge, the next slice is all the way over to the left but one position down.

The preceding example demonstrates a two-dimensional stride. If the input matrix is three-dimensional, the stride would also be three-dimensional.

## structural risk minimization (SRM)

An algorithm that balances two goals:

- The desire to build the most predictive model (for example, lowest loss).
- The desire to keep the model as simple as possible (for example, strong regularization).

For example, a function that minimizes loss+regularization on the training set is a structural risk minimization algorithm.

Contrast with **empirical risk minimization**.

## subsampling

See **pooling**.

## summary

In TensorFlow, a value or set of values calculated at a particular
**step**, usually used for tracking model metrics during training.

## supervised machine learning

Training a **model** from input data and its corresponding
**labels**. Supervised machine learning is analogous to a student
learning a subject by studying a set of questions and their corresponding
answers. After mastering the mapping between questions and answers, the
student can then provide answers to new (never-before-seen) questions on
the same topic. Compare with
**unsupervised machine learning**.

## synthetic feature

A **feature** not present among the input features, but
created from one or more of them. Kinds of synthetic features include:

**Bucketing**a continuous feature into range bins.- Multiplying (or dividing) one feature value by other feature value(s) or by itself.
- Creating a
**feature cross**.

Features created by **normalizing** or **scaling**
alone are not considered synthetic features.

## T

## tabular Q-learning

In reinforcement learning, implementing **Q-learning**
by using a table to store the **Q-functions** for every
combination of **state** and **action**.

## target

Synonym for **label**.

## target network

In **Deep Q-learning**, a neural network that is a stable
approximation of the main neural network, where the main neural network
implements either a **Q-function** or a **policy**.
Then, you can train the main network on the Q-values predicted by the target
network. Therefore, you prevent the feedback loop that occurs when the main
network trains on Q-values predicted by itself. By avoiding this feedback,
training stability increases.

## temporal data

Data recorded at different points in time. For example, winter coat sales recorded for each day of the year would be temporal data.

## Tensor

The primary data structure in TensorFlow programs. Tensors are N-dimensional (where N could be very large) data structures, most commonly scalars, vectors, or matrices. The elements of a Tensor can hold integer, floating-point, or string values.

## TensorBoard

The dashboard that displays the summaries saved during the execution of one or more TensorFlow programs.

## TensorFlow

A large-scale, distributed, machine learning platform. The term also refers to the base API layer in the TensorFlow stack, which supports general computation on dataflow graphs.

Although TensorFlow is primarily used for machine learning, you may also use TensorFlow for non-ML tasks that require numerical computation using dataflow graphs.

## TensorFlow Playground

A program that visualizes how different
**hyperparameters** influence model
(primarily neural network) training.
Go to
http://playground.tensorflow.org
to experiment with TensorFlow Playground.

## TensorFlow Serving

A platform to deploy trained models in production.

## Tensor Processing Unit (TPU)

An application-specific integrated circuit (ASIC) that optimizes the
performance of machine learning workloads. These ASICs are deployed as
multiple **TPU chips** on a **TPU device**.

## Tensor rank

See **rank (Tensor)**.

## Tensor shape

The number of elements a **Tensor** contains in various dimensions.
For example, a [5, 10] Tensor has a shape of 5 in one dimension and 10
in another.

## Tensor size

The total number of scalars a **Tensor** contains. For example, a
[5, 10] Tensor has a size of 50.

## termination condition

In reinforcement learning, the conditions that determine when an
**episode** ends, such as when the agent reaches a certain state
or exceeds a threshold number of state transitions.
For example, in tic-tac-toe (also
known as noughts and crosses), an episode terminates either when a player marks
three consecutive spaces or when all spaces are marked.

## test set

The subset of the dataset that you use to test your **model**
after the model has gone through initial vetting by the validation set.

Contrast with **training set** and
**validation set**.

## tf.Example

A standard protocol buffer for describing input data for machine learning model training or inference.

## tf.keras

An implementation of **Keras** integrated into
**TensorFlow**.

## time series analysis

A subfield of machine learning and statistics that analyzes
**temporal data**. Many types of machine learning
problems require time series analysis, including classification, clustering,
forecasting, and anomaly detection. For example, you could use
time series analysis to forecast the future sales of winter coats by month
based on historical sales data.

## timestep

One "unrolled" cell within a
**recurrent neural network**.
For example, the following figure shows three timesteps (labeled with
the subscripts t-1, t, and t+1):

## token

In a **language model**, the atomic unit that the model is
training on and making predictions on. A token is typically one of the
following:

- a word—for example, the phrase "dogs like cats" consists of three word tokens: "dogs", "like", and "cats".
- a character—for example, the phrase "bike fish" consists of nine character tokens. (Note that the blank space counts as one of the tokens.)
- subwords—in which a single word can be a single token or multiple tokens. A subword consists of a root word, a prefix, or a suffix. For example, a language model that uses subwords as tokens might view the word "dogs" as two tokens (the root word "dog" and the plural suffix "s"). That same language model might view the single word "taller" as two subwords (the root word "tall" and the suffix "er").

In domains outside of language models, tokens can represent other kinds of atomic units. For example, in computer vision, a token might be a subset of an image.

## tower

A component of a **deep neural network** that
is itself a deep neural network without an output layer. Typically,
each tower reads from an independent data source. Towers are independent
until their output is combined in a final layer.

## TPU

Abbreviation for **Tensor Processing Unit**.

## TPU chip

A programmable linear algebra accelerator with on-chip high bandwidth memory
that is optimized for machine learning workloads.
Multiple TPU chips are deployed on a **TPU device**.

## TPU device

A printed circuit board (PCB) with multiple **TPU chips**,
high bandwidth network interfaces, and system cooling hardware.

## TPU master

The central coordination process running on a host machine that sends and
receives data, results, programs, performance, and system health information
to the **TPU workers**. The TPU master also manages the setup
and shutdown of **TPU devices**.

## TPU node

A TPU resource on Google Cloud Platform with a specific
**TPU type**. The TPU node connects to your
VPC Network from a
peer VPC network.
TPU nodes are a resource defined in the
Cloud TPU API.

## TPU Pod

A specific configuration of **TPU devices** in a Google
data center. All of the devices in a TPU pod are connected to one another
over a dedicated high-speed network. A TPU Pod is the largest configuration of
**TPU devices** available for a specific TPU version.

## TPU resource

A TPU entity on Google Cloud Platform that you create, manage, or consume. For
example, **TPU nodes** and **TPU types** are
TPU resources.

## TPU slice

A TPU slice is a fractional portion of the **TPU devices** in
a **TPU Pod**. All of the devices in a TPU slice are connected
to one another over a dedicated high-speed network.

## TPU type

A configuration of one or more **TPU devices** with a specific
TPU hardware version. You select a TPU type when you create
a **TPU node** on Google Cloud Platform. For example, a `v2-8`

TPU type is a single TPU v2 device with 8 cores. A `v3-2048`

TPU type has 256
networked TPU v3 devices and a total of 2048 cores. TPU types are a resource
defined in the
Cloud TPU API.

## TPU worker

A process that runs on a host machine and executes machine learning programs
on **TPU devices**.

## training

The process of determining the ideal **parameters** comprising
a model.

## training set

The subset of the dataset used to train a model.

Contrast with **validation set** and
**test set**.

## trajectory

In reinforcement learning, a sequence of
tuples that represent
a sequence of **state** transitions of the **agent**,
where each tuple corresponds to the state, **action**,
**reward**, and next state for a given state transition.

## transfer learning

Transferring information from one machine learning task to another.
For example, in multi-task learning, a single model solves multiple tasks,
such as a **deep model** that has different output nodes for
different tasks. Transfer learning might involve transferring knowledge
from the solution of a simpler task to a more complex one, or involve
transferring knowledge from a task where there is more data to one where
there is less data.

Most machine learning systems solve a *single* task. Transfer learning is a
baby step towards artificial intelligence in which a single program can solve
*multiple* tasks.

## Transformer

A **neural network** architecture developed at Google that
relies on **self-attention** mechanisms to transform a
sequence of input **embeddings** into a sequence of output
embeddings without relying on **convolutions** or
**recurrent neural networks**. A Transformer can be
viewed as a stack of self-attention layers.

A Transformer can include any of the following:

An **encoder** transforms a sequence of embeddings into a new sequence of the
same length. An encoder includes N identical layers, each of which contains two
sub-layers. These two sub-layers are applied at each position of the input
embedding sequence, transforming each element of the sequence into a new
embedding. The first encoder sub-layer aggregates information from across the
input sequence. The second encoder sub-layer transforms the aggregated
information into an output embedding.

A **decoder** transforms a sequence of input embeddings into a sequence of
output embeddings, possibly with a different length. A decoder also includes
N identical layers with three sub-layers, two of which are similar to the
encoder sub-layers. The third decoder sub-layer takes the output of the
encoder and applies the **self-attention** mechanism to
gather information from it.

The blog post Transformer: A Novel Neural Network Architecture for Language Understanding provides a good introduction to Transformers.

## translational invariance

In an image classification problem, an algorithm's ability to successfully classify images even when the position of objects within the image changes. For example, the algorithm can still identify a dog, whether it is in the center of the frame or at the left end of the frame.

See also **size invariance** and
**rotational invariance**.

## trigram

An **N-gram** in which N=3.

## true negative (TN)

An example in which the model *correctly* predicted the
**negative class**. For example, the model inferred that
a particular email message was not spam, and that email message really was
not spam.

## true positive (TP)

An example in which the model *correctly* predicted the
**positive class**. For example, the model inferred that
a particular email message was spam, and that email message really was spam.

## true positive rate (TPR)

Synonym for **recall**. That is:

True positive rate is the y-axis in an **ROC curve**.

## U

## unawareness (to a sensitive attribute)

A situation in which **sensitive attributes** are
present, but not included in the training data. Because sensitive attributes
are often correlated with other attributes of one’s data, a model trained
with unawareness about a sensitive attribute could still have
**disparate impact** with respect to that attribute,
or violate other **fairness constraints**.

## underfitting

Producing a model with poor predictive ability because the model hasn't captured the complexity of the training data. Many problems can cause underfitting, including:

- Training on the wrong set of features.
- Training for too few epochs or at too low a learning rate.
- Training with too high a regularization rate.
- Providing too few hidden layers in a deep neural network.

## undersampling

Removing **examples** from the
**majority class** in a
**class-imbalanced dataset** in order to
create a more balanced **training set**.

For example, consider a dataset in which the ratio of the majority class to
the **minority class** is 20:1. To overcome this class
imbalance, you could create a training set consisting of *all* of the minority
class examples but only a *tenth* of the majority class examples, which would
create a training-set class ratio of 2:1. Thanks to undersampling, this more
balanced training set *might* produce a better model. Alternatively, this
more balanced training set might contain insufficient examples to train an
effective model.

Contrast with **oversampling**.

## unidirectional

A system that only evaluates the text that *precedes* a target section of text.
In contrast, a bidirectional system evaluates both the
text that *precedes* and *follows* a target section of text.
See **bidirectional** for more details.

## unidirectional language model

A **language model** that bases its probabilities only on the
**tokens** appearing *before*, not *after*, the target token(s).
Contrast with **bidirectional language model**.

## unlabeled example

An example that contains **features** but no **label**.
Unlabeled examples are the input to **inference**. In
**semi-supervised** and
**unsupervised** learning,
unlabeled examples are used during training.

## unsupervised machine learning

Training a **model** to find patterns in a dataset, typically an
unlabeled dataset.

The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example, an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example, to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example, in domains such as anti-abuse and fraud, clusters can help humans better understand the data.

Another example of unsupervised machine learning is principal component analysis (PCA). For example, applying PCA on a dataset containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids.

Compare with **supervised machine learning**.

## upweighting

Applying a weight to the **downsampled** class equal
to the factor by which you downsampled.

## user matrix

In **recommendation systems**, an
**embedding** generated by
**matrix factorization**
that holds latent signals about user preferences.
Each row of the user matrix holds information about the relative
strength of various latent signals for a single user.
For example, consider a movie recommendation system. In this system,
the latent signals in the user matrix might represent each user's interest
in particular genres, or might be harder-to-interpret signals that involve
complex interactions across multiple factors.

The user matrix has a column for each latent feature and a row for each user. That is, the user matrix has the same number of rows as the target matrix that is being factorized. For example, given a movie recommendation system for 1,000,000 users, the user matrix will have 1,000,000 rows.

## V

## validation

A process used, as part of **training**, to evaluate
the quality of a **machine learning** model
using the **validation set**. Because the validation
set is disjoint from the training set, validation helps ensure that the
model’s performance generalizes beyond the training set.

Contrast with **test set**.

## validation set

A subset of the dataset—disjoint from the training set—used
in **validation**.

Contrast with **training set**
and **test set**.

## vanishing gradient problem

The tendency for the gradients of early **hidden layers**
of some **deep neural networks** to become
surprisingly flat (low). Increasingly lower gradients result in increasingly
smaller changes to the weights on nodes in a deep neural network, leading to
little or no learning. Models suffering from the vanishing gradient problem
become difficult or impossible to train.
**Long Short-Term Memory** cells address this issue.

Compare to **exploding gradient problem**.

## W

## Wasserstein loss

One of the loss functions commonly used in
**generative adversarial networks**,
based on the **earth mover's distance** between
the distribution of generated data and real data.

## weight

A coefficient for a **feature** in a linear model, or an edge
in a deep network. The goal of training a linear model is to determine
the ideal weight for each feature. If a weight is 0, then its corresponding
feature does not contribute to the model.

## Weighted Alternating Least Squares (WALS)

An algorithm for minimizing the objective function during
**matrix factorization** in
**recommendation systems**, which allows a
downweighting of the missing examples. WALS minimizes the weighted
squared error between the original matrix and the reconstruction by
alternating between fixing the row factorization and column factorization.
Each of these optimizations can be solved by least squares
**convex optimization**. For details, see the
Recommendation Systems course

## wide model

A linear model that typically has many
**sparse input features**. We refer to it as "wide" since
such a model is a special type of **neural network** with a
large number of inputs that connect directly to the output node. Wide models
are often easier to debug and inspect than deep models. Although wide models
cannot express nonlinearities through **hidden layers**,
they can use transformations such as
**feature crossing** and
**bucketization** to model nonlinearities in different ways.

Contrast with **deep model**.

## width

The number of **neurons** in a particular **layer**
of a **neural network**.

## word embedding

**Representing** each word in a word set within an
**embedding**; that is, representing each word as a vector of
floating-point values between 0.0 and 1.0. Words with similar meanings have
more-similar representations than words with different meanings. For example,
*carrots*, *celery*, and *cucumbers* would all have relatively similar
representations, which would be very different from the representations of
*airplane*, *sunglasses*, and *toothpaste*.