Exercises

This page lists the exercises in Machine Learning Crash Course.

The majority of the Programming Exercises use the California housing data set.

All

Preliminaries

  • hello_world.ipynb
  • tensorflow_programming_concepts.ipynb
  • creating_and_manipulating_tensors.ipynb
  • intro_to_pandas.ipynb

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

  • first_steps_with_tensor_flow.ipynb
  • synthetic_features_and_outliers.ipynb

Training and Test Sets

Validation

Representation

  • feature_sets.ipynb

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

  • multi-class_classification_of_handwritten_digits.ipynb

Embeddings

  • intro_to_sparse_data_and_embeddings.ipynb

Fairness

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Programming

Preliminaries

  • hello_world.ipynb
  • tensorflow_programming_concepts.ipynb
  • creating_and_manipulating_tensors.ipynb
  • intro_to_pandas.ipynb

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

  • first_steps_with_tensor_flow.ipynb
  • synthetic_features_and_outliers.ipynb

Training and Test Sets

Validation

Representation

  • feature_sets.ipynb

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

  • multi-class_classification_of_handwritten_digits.ipynb

Embeddings

  • intro_to_sparse_data_and_embeddings.ipynb

Fairness

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Check Your Understanding

Preliminaries

  • hello_world.ipynb
  • tensorflow_programming_concepts.ipynb
  • creating_and_manipulating_tensors.ipynb
  • intro_to_pandas.ipynb

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

  • first_steps_with_tensor_flow.ipynb
  • synthetic_features_and_outliers.ipynb

Training and Test Sets

Validation

Representation

  • feature_sets.ipynb

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

  • multi-class_classification_of_handwritten_digits.ipynb

Embeddings

  • intro_to_sparse_data_and_embeddings.ipynb

Fairness

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Playground

Preliminaries

  • hello_world.ipynb
  • tensorflow_programming_concepts.ipynb
  • creating_and_manipulating_tensors.ipynb
  • intro_to_pandas.ipynb

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

  • first_steps_with_tensor_flow.ipynb
  • synthetic_features_and_outliers.ipynb

Training and Test Sets

Validation

Representation

  • feature_sets.ipynb

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

  • multi-class_classification_of_handwritten_digits.ipynb

Embeddings

  • intro_to_sparse_data_and_embeddings.ipynb

Fairness

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies