그림 1. 비선형 분류 문제입니다. 선형 함수로는 모든 파란색 점을 오렌지색 점과 명확하게 구분할 수 없습니다.
'비선형' 가중치로는 라벨을 정확하게 예측할 수 없다는
\(b + w_1x_1 + w_2x_2\)형태의 모델입니다. 다시 말해, '결정 표면'은 선이 아닙니다.
하지만 특성 $x_1$과 $x_2$에 특성 교차를 실행하면 선형 모델을 사용하여 두 특성 간의 비선형 관계를 나타낼 수 있습니다. $b + w_1x_1 + w_2x_2 + w_3x_3$, 여기서 $x_3$은 $x_1$과 $x_2$ 간의 특성 교차입니다.
그림 2. 특성 교차 x1x2를 추가하면 선형 모델은 파란색 점을 주황색 점과 구분하는 타원형 도형을 학습할 수 있습니다.
다음 데이터 세트를 살펴보겠습니다.
그림 3. 더 어려운 비선형 분류 문제
특성 교차 연습에서 이 데이터에 선형 모델을 맞추기 위해 올바른 특성 교차를 결정하는 데 약간의 노력과 실험이 필요했다는 것을 기억할 수 있습니다.
하지만 이 모든 실험을 직접 할 필요가 없다면 어떻게 해야 할까요?
신경망은
찾아낼 수 있도록 설계된 모델 아키텍처의
nonlinear
데이터 패턴을 나타냅니다. 신경망 학습 도중에는
모델을 자동으로
입력 데이터에서 최적의 특성 교차를 학습하여
손실이 발생할 수 있습니다.
[null,null,["최종 업데이트: 2025-07-27(UTC)"],[[["\u003cp\u003eThis module explores neural networks, a model architecture designed to automatically identify nonlinear patterns in data, eliminating the need for manual feature cross experimentation.\u003c/p\u003e\n"],["\u003cp\u003eYou will learn the fundamental components of a deep neural network, including nodes, hidden layers, and activation functions, and how they contribute to prediction.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers the training process of neural networks, using the backpropagation algorithm to optimize predictions and minimize loss.\u003c/p\u003e\n"],["\u003cp\u003eAdditionally, you will gain insights into how neural networks handle multi-class classification problems using one-vs.-all and one-vs.-one approaches.\u003c/p\u003e\n"],["\u003cp\u003eThis module builds on prior knowledge of machine learning concepts such as linear and logistic regression, classification, and working with numerical and categorical data.\u003c/p\u003e\n"]]],[],null,["# Neural networks\n\n| **Estimated module length:** 75 minutes\n| **Learning objectives**\n|\n| - Explain the motivation for building neural networks, and the use cases they address.\n| - Define and explain the function of the key components of a deep neural network architecture:\n| - **[Nodes](/machine-learning/glossary#node-neural-network)**\n| - **[Hidden layers](/machine-learning/glossary#hidden_layer)**\n| - **[Activation functions](/machine-learning/glossary#activation_function)**\n| - Develop intuition around how neural network predictions are made, by stepping through the inference process.\n| - Build a high-level intuition of how neural networks are trained, using the backpropagation algorithm.\n| - Explain how neural networks can be used to perform two types of multi-class classification: one-vs.-all and one-vs.-one.\n| **Prerequisites:**\n|\n| This module assumes you are familiar with the concepts covered in the\n| following modules:\n|\n| - [Introduction to Machine Learning](/machine-learning/intro-to-ml)\n| - [Linear regression](/machine-learning/crash-course/linear-regression)\n| - [Logistic regression](/machine-learning/crash-course/logistic-regression)\n| - [Classification](/machine-learning/crash-course/classification)\n| - [Working with numerical data](/machine-learning/crash-course/numerical-data)\n| - [Working with categorical data](/machine-learning/crash-course/categorical-data)\n| - [Datasets, generalization, and overfitting](/machine-learning/crash-course/overfitting)\n\nYou may recall from the\n[Feature cross exercises](/machine-learning/crash-course/categorical-data/feature-cross-exercises)\nin the [Categorical data module](/machine-learning/crash-course/categorical-data),\nthat the following classification problem is nonlinear:\n**Figure 1.** Nonlinear classification problem. A linear function cannot cleanly separate all the blue dots from the orange dots.\n\n\"Nonlinear\" means that you can't accurately predict a label with a\nmodel of the form \\\\(b + w_1x_1 + w_2x_2\\\\). In other words, the\n\"decision surface\" is not a line.\n\nHowever, if we perform a feature cross on our features $x_1$ and $x_2$, we can\nthen represent the nonlinear relationship between the two features using a\n[**linear model**](/machine-learning/glossary#linear-model):\n$b + w_1x_1 + w_2x_2 + w_3x_3$ where $x_3$ is the feature cross between\n$x_1$ and $x_2$:\n**Figure 2.** By adding the feature cross *x* ~1~*x* ~2~, the linear model can learn a hyperbolic shape that separates the blue dots from the orange dots.\n\nNow consider the following dataset:\n**Figure 3.** A more difficult nonlinear classification problem.\n\nYou may also recall from the [Feature cross exercises](/machine-learning/crash-course/categorical-data/feature-cross-exercises)\nthat determining the correct feature crosses to fit a linear model to this data\ntook a bit more effort and experimentation.\n\nBut what if you didn't have to do all that experimentation yourself?\n[**Neural networks**](/machine-learning/glossary#neural_network) are a family\nof model architectures designed to find\n[**nonlinear**](/machine-learning/glossary#nonlinear)\npatterns in data. During training of a neural network, the\n[**model**](/machine-learning/glossary#model) automatically\nlearns the optimal feature crosses to perform on the input data to minimize\nloss.\n\nIn the following sections, we'll take a closer look at how neural networks work.\n| **Key terms:**\n|\n| - [Activation function](/machine-learning/glossary#activation_function)\n| - [Hidden layer](/machine-learning/glossary#hidden_layer)\n| - [Linear model](/machine-learning/glossary#linear-model)\n| - [Model](/machine-learning/glossary#model)\n| - [Neural network](/machine-learning/glossary#neural_network)\n| - [Nodes](/machine-learning/glossary#node-neural-network)\n- [Nonlinear](/machine-learning/glossary#nonlinear) \n[Help Center](https://support.google.com/machinelearningeducation)"]]
