/pytorch-tutorials-examples-and-books

PyTorch1.0 tutorials, examples and some books I found [不断更新中...]整理的PyTorch 1.0 最新版教程、例子和书籍

Primary LanguageJupyter Notebook

PyTorch tutorials, examples and books

Table of Contents / 目录:

PyTorch 版本变化及迁移指南

PyTorch for Numpy users 给Numpy用户的PyTorch指南

  • 表格过长，请点击这里

PyTorch 1.0 tutorials and examples

Books and slides about PyTorch 书籍、PPT等

Note: some of these are old version; 下面的书籍部分还不是1.0版本。。毕竟1.0刚出所以书籍较少。。

  • Deep Learning Toolkits II pytorch example
  • PyTorch 1.0 Bringing research and production together Presentation
  • Deep Learning with PyTorch - Packet Vishnu Subramanian
  • PyTorch深度学习实战 - 侯宜军（pdf）
  • PyTorch深度学习实战 - 侯宜军（epub）
  • 深度学习之Pytorch - 廖星宇
  • 深度学习之PyTorch实战计算机视觉 - 唐进民
  • pytorch卷积、反卷积 - download from internet
  • A brief summary of the PTDC ’18 PyTorch 1.0 Preview and Promise - Hacker Noon
  • PyTorch_tutorial_0.0.4_余霆嵩
  • 深度学习入门之PyTorch - 廖星宇（有目录）
  • 深度学习框架PyTorch：入门与实践 - 陈云
  • PyTorch 0.4 中文文档 - 翻译
  • pytorch 0.4 - tutorial - 有目录版
  • Automatic differentiation in PyTorch - paper

PyTorch深度学习：60分钟入门与实战

Learning PyTorch with Examples 用例子学习PyTorch

计算机视觉与PyTorch

PyTorch1.0-Zero-To-All

  • Slides-newest from Google Drive
    • Lecture 01_ Overview.pptx
    • Lecture 02_ Linear Model.pptx
    • Lecture 03_ Gradient Descent.pptx
    • Lecture 04_ Back-propagation and PyTorch autograd.pptx
    • Lecture 05_ Linear regression in PyTorch way.pptx
    • Lecture 06_ Logistic Regression.pptx
    • Lecture 07_ Wide _ Deep.pptx
    • Lecture 08_ DataLoader.pptx
    • Lecture 09_ Softmax Classifier.pptx
    • Lecture 10_ Basic CNN.pptx
    • Lecture 11_ Advanced CNN.pptx
    • Lecture 12_ RNN.pptx
    • Lecture 13_ RNN II.pptx
    • Lecture 14_ Seq2Seq.pptx
    • Lecture 15_ NSML, Smartest ML Platform.pptx

Udacity: Deep Learning with PyTorch

  • Part 1: Introduction to PyTorch and using tensors
  • Part 2: Building fully-connected neural networks with PyTorch
  • Part 3: How to train a fully-connected network with backpropagation on MNIST
  • Part 4: Exercise - train a neural network on Fashion-MNIST
  • Part 5: Using a trained network for making predictions and validating networks
  • Part 6: How to save and load trained models
  • Part 7: Load image data with torchvision, also data augmentation
  • Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats

Deep Learning Course Slides and Handout - fleuret.org

  • 1-1-from-anns-to-deep-learning.pdf
  • 1-2-current-success.pdf
  • 1-3-what-is-happening.pdf
  • 1-4-tensors-and-linear-regression.pdf
  • 1-5-high-dimension-tensors.pdf
  • 1-6-tensor-internals.pdf
  • 2-1-loss-and-risk.pdf
  • 2-2-overfitting.pdf
  • 2-3-bias-variance-dilemma.pdf
  • 2-4-evaluation-protocols.pdf
  • 2-5-basic-embeddings.pdf
  • 3-1-perceptron.pdf
  • 3-2-LDA.pdf
  • 3-3-features.pdf
  • 3-4-MLP.pdf
  • 3-5-gradient-descent.pdf
  • 3-6-backprop.pdf
  • 4-1-DAG-networks.pdf
  • 4-2-autograd.pdf
  • 4-3-modules-and-batch-processing.pdf
  • 4-4-convolutions.pdf
  • 4-5-pooling.pdf
  • 4-6-writing-a-module.pdf
  • 5-1-cross-entropy-loss.pdf
  • 5-2-SGD.pdf
  • 5-3-optim.pdf
  • 5-4-l2-l1-penalties.pdf
  • 5-5-initialization.pdf
  • 5-6-architecture-and-training.pdf
  • 5-7-writing-an-autograd-function.pdf
  • 6-1-benefits-of-depth.pdf
  • 6-2-rectifiers.pdf
  • 6-3-dropout.pdf
  • 6-4-batch-normalization.pdf
  • 6-5-residual-networks.pdf
  • 6-6-using-GPUs.pdf
  • 7-1-CV-tasks.pdf
  • 7-2-image-classification.pdf
  • 7-3-object-detection.pdf
  • 7-4-segmentation.pdf
  • 7-5-dataloader-and-surgery.pdf
  • 8-1-looking-at-parameters.pdf
  • 8-2-looking-at-activations.pdf
  • 8-3-visualizing-in-input.pdf
  • 8-4-optimizing-inputs.pdf
  • 9-1-transposed-convolutions.pdf
  • 9-2-autoencoders.pdf
  • 9-3-denoising-and-variational-autoencoders.pdf
  • 9-4-NVP.pdf
  • 10-1-GAN.pdf
  • 10-2-Wasserstein-GAN.pdf
  • 10-3-conditional-GAN.pdf
  • 10-4-persistence.pdf
  • 11-1-RNN-basics.pdf
  • 11-2-LSTM-and-GRU.pdf
  • 11-3-word-embeddings-and-translation.pdf

How to run? 推荐的运行方式

Some code in this repo is separated in blocks using #%%. A block is as same as a cell in Jupyter Notebook. So editors/IDEs supporting this functionality is recommanded.

Such as: