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PyTorch tutorials, examples and books

Table of Contents / 目录:

PyTorch 1.x 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.x版本。

该目录更新可能有延迟,全部资料请看该文件夹内文件

  • Automatic differentiation in PyTorch.pdf
  • A brief summary of the PTDC ’18 PyTorch 1.0 Preview and Promise - Hacker Noon.pdf
  • Deep Architectures.pdf
  • Deep Architectures.pptx
  • Deep Learning Toolkits II pytorch example.pdf
  • Deep Learning with PyTorch - Vishnu Subramanian.pdf
  • Deep-Learning-with-PyTorch.pdf
  • Deep_Learning_with_PyTorch_Quick_Start_Guide.pdf
  • First steps towards deep learning with pytorch.pdf
  • Introduction to Tensorflow, PyTorch and Caffe.pdf
  • pytorch 0.4 - tutorial - 有目录版.pdf
  • PyTorch 0.4 中文文档 - 翻译.pdf
  • PyTorch 1.0 Bringing research and production together Presentation.pdf
  • PyTorch Recipes - A Problem-Solution Approach - Pradeepta Mishra.pdf
  • PyTorch under the hood A guide to understand PyTorch internals.pdf
  • pytorch-internals.pdf
  • PyTorch_tutorial_0.0.4_余霆嵩.pdf
  • PyTorch_tutorial_0.0.5_余霆嵩.pdf
  • pytorch卷积、反卷积 - download from internet.pdf
  • PyTorch深度学习实战 - 侯宜军.epub
  • PyTorch深度学习实战 - 侯宜军.pdf
  • 深度学习之Pytorch - 廖星宇.pdf
  • 深度学习之PyTorch实战计算机视觉 - 唐进民.pdf
  • 深度学习入门之PyTorch - 廖星宇(有目录).pdf
  • 深度学习框架PyTorch:入门与实践 - 陈云.pdf
  • Udacity: Deep Learning with PyTorch
    展开查看
    * 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
      
  • PyTorch-Zero-To-All:Slides-newest from Google Drive
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    * 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
      
  • Deep Learning Course Slides and Handout - fleuret.org
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    * 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
      

以下是一些独立的教程

1) PyTorch深度学习:60分钟入门与实战

展开查看



2) Learning PyTorch with Examples 用例子学习PyTorch

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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:


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