D2L - 4. Linear Neural Networks for Classification

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4. Linear Neural Networks for Classification — Dive into Deep Learning 1.0.0-beta0 documentation

4. Linear Neural Networks for Classification

Now that you have worked through all of the mechanics you are ready to apply these skills to broader kinds of tasks. Even as we pivot towards classification, most of the plumbing remains the same: loading the data, passing it through the model, generating output, calculating the loss, taking gradients with respect to weights, and updating the model. However, the precise form of the targets, the parameterization of the output layer, and the choice of loss function will adapt to suit the classification setting.

 

이제 모든 메커니즘을 살펴보았으므로 이러한 기술을 더 광범위한 종류의 작업에 적용할 준비가 되었습니다. 분류로 전환하더라도 대부분의 배관 작업은 동일하게 유지됩니다. 즉, 데이터 로드, 모델에 데이터 전달, 출력 생성, 손실 계산, 가중치에 대한 그래디언트 적용 및 모델 업데이트입니다. 그러나 대상의 정확한 형태, 출력 계층의 매개변수화 및 손실 함수 선택은 분류 설정에 맞게 조정됩니다.

 

 

• 4.1. Softmax Regression
  • 4.1.1. Classification
  • 4.1.2. Loss Function
  • 4.1.3. Information Theory Basics
  • 4.1.4. Summary and Discussion
  • 4.1.5. Exercises
• 4.2. The Image Classification Dataset
  • 4.2.1. Loading the Dataset
  • 4.2.2. Reading a Minibatch
  • 4.2.3. Visualization
  • 4.2.4. Summary
  • 4.2.5. Exercises
• 4.3. The Base Classification Model
  • 4.3.1. The Classifier Class
  • 4.3.2. Accuracy
  • 4.3.3. Summary
  • 4.3.4. Exercises
• 4.4. Softmax Regression Implementation from Scratch
  • 4.4.1. The Softmax
  • 4.4.2. The Model
  • 4.4.3. The Cross-Entropy Loss
  • 4.4.4. Training
  • 4.4.5. Prediction
  • 4.4.6. Summary
  • 4.4.7. Exercises
• 4.5. Concise Implementation of Softmax Regression
  • 4.5.1. Defining the Model
  • 4.5.2. Softmax Revisited
  • 4.5.3. Training
  • 4.5.4. Summary
  • 4.5.5. Exercises
• 4.6. Generalization in Classification
  • 4.6.1. The Test Set
  • 4.6.2. Test Set Reuse
  • 4.6.3. Statistical Learning Theory
  • 4.6.4. Summary
  • 4.6.5. Exercises
• 4.7. Environment and Distribution Shift
  • 4.7.1. Types of Distribution Shift
  • 4.7.2. Examples of Distribution Shift
  • 4.7.3. Correction of Distribution Shift
  • 4.7.4. A Taxonomy of Learning Problems
  • 4.7.5. Fairness, Accountability, and Transparency in Machine Learning
  • 4.7.6. Summary
  • 4.7.7. Exercises