Dive into Deep Learning PyTorch

This repository contains my learning notes for Dive into Deep Learning. The code is specially adapted for making it work in PyCharm.

Chapters

• 3. Linear Neural Networks
  • Linear Regression
  • Softmax Regression
• 4. Multilayer Perceptrons
  • Multilayer Perceptrons
  • Weight Decay
  • Dropout
• 6. Convolutional Neural Networks
  • Convolutional Neural Networks (LeNet)
• 7. Modern Convolutional Neural Networks
  • Deep Convolutional Neural Networks (AlexNet)
  • Networks Using Blocks (VGG)
  • Network in Network (NiN)
  • Networks with Parallel Concatenations (GoogLeNet)
  • Batch Normalization
  • Residual Networks (ResNet)
  • Densely Connected Networks (DenseNet)
• Kaggle
  • Predicting House Prices

Reference

1. Dive into Deep Learning | Github Repo
2. 动手学深度学习(PyTorch版) | Github仓库
3. Others
   1. PyTorch Documents
   2. PyTorch Tutorials

Cite

if you use this work or code for your research, please cite the original book using the following bibtex entry:

@book{zhang2020dive,
    title={Dive into Deep Learning},
    author={Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola},
    note={\url{https://d2l.ai}},
    year={2020}
}