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Dive into Deep Learning
Table Of Contents
まえがき
インストール
表記法
1. はじめに
2. 事前準備
3. Deep Learning Basics
3.1. Linear Regression
3.2. Linear Regression Implementation from Scratch
3.3. Concise Implementation of Linear Regression
3.4. Softmax Regression
3.5. Image Classification Data (Fashion-MNIST)
3.6. Implementation of Softmax Regression from Scratch
3.7. Concise Implementation of Softmax Regression
3.8. Multilayer Perceptron
3.9. Implementation of Multilayer Perceptron from Scratch
3.10. Concise Implementation of Multilayer Perceptron
3.11. Model Selection, Underfitting and Overfitting
3.12. Weight Decay
3.13. Dropout
3.14. Forward Propagation, Back Propagation, and Computational Graphs
3.15. Numerical Stability and Initialization
3.16. Environment
3.17. Predicting House Prices on Kaggle
Dive into Deep Learning
Table Of Contents
まえがき
インストール
表記法
1. はじめに
2. 事前準備
3. Deep Learning Basics
3.1. Linear Regression
3.2. Linear Regression Implementation from Scratch
3.3. Concise Implementation of Linear Regression
3.4. Softmax Regression
3.5. Image Classification Data (Fashion-MNIST)
3.6. Implementation of Softmax Regression from Scratch
3.7. Concise Implementation of Softmax Regression
3.8. Multilayer Perceptron
3.9. Implementation of Multilayer Perceptron from Scratch
3.10. Concise Implementation of Multilayer Perceptron
3.11. Model Selection, Underfitting and Overfitting
3.12. Weight Decay
3.13. Dropout
3.14. Forward Propagation, Back Propagation, and Computational Graphs
3.15. Numerical Stability and Initialization
3.16. Environment
3.17. Predicting House Prices on Kaggle
Index