Dive into Deep Learning¶
Dive into Deep Learning
An interactive deep learning book for students, engineers, and
researchers.
The contents are under revision.
We thank all the community contributors
for making this open source book better for everyone.
Contribute to the book.
Each section is an executable Jupyter notebook
You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning.
Mathematics + Figures + Code
We offer an interactive learning experience with mathematics, figures, code, text, and discussions, where concepts and techniques are illustrated and implemented with experiments on real data sets.
Active community support
You can discuss and learn with thousands of peers in the community through the link provided in each section.
Table of contents
- 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
