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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
  • 以下是一些独立的教程
    • 1) PyTorch深度学习：60分钟入门与实战
    • 2) Learning PyTorch with Examples 用例子学习PyTorch
  • How to run? 推荐的运行方式

• 0.PyTorch 版本变化及迁移指南
• 1.PyTorch_for_Numpy_Users 给Numpy用户的PyTorch指南
• 2.PyTorch_Basics PyTorch基础
• 3.Linear_Regression 线性回归
• 4.Logistic_Regression Logistic 回归
• 5.Optimizer 优化器
• 6.Neural_Network 神经网络
• 7.Convolutional_Neural_Network(CNN) 卷积神经网络
• 8.Famous_CNN 经典的CNN网络
• 9.Using_Pretrained_models 使用预训练的模型
• 10.Dataset_and_Dataloader 自定义数据读取
• 11.Custom_Dataset_example 定义自己的数据集
• 12.Visdom_Visualization visdom可视化
• 13.Tensorboard_Visualization tensorboard可视化
• 14.Semantic_Segmentation 语义分割
• 15.Transfer_Learning 迁移学习
• 16.Neural_Style(StyleTransfer) 风格迁移
• A.计算机视觉与PyTorch
  • PyTorch与计算机视觉简要总结
    • Markdown version
    • Notebook version
• B.PyTorch概览

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
  展开查看

  * 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

什么是PyTorch？（What is PyTorch?）
入门
张量
运算
NumPy桥
将torch的Tensor转化为NumPy数组
将NumPy数组转化为Torch张量
CUDA上的张量
Autograd：自动求导
张量
梯度
神经网络（Neural Networks）
定义网络
损失函数
反向传播
更新权重
训练分类器（Training a Classifier）
数据呢？
训练一个图片分类器
1.加载并标准化CIFAR10
2.定义卷积神经网络
3.定义损失函数和优化器
4.训练网络
5.使用测试数据测试网络
在GPU上训练
在多GPU上训练
接下来要做什么？
选读：数据并行处理（Optional: Data Parallelism）
导入和参数
虚拟数据集
简单模型
创建一个模型和数据并行
运行模型
结果
2个GPU
3个GPU
8个GPU
总结

张量(Tensors)
热身：使用NumPy
PyTorch：张量(Tensors)
自动求导(Autograd)
PyTorch：自动求导(Autograd)
PyTorch：定义自己的自动求导函数
TensorFlow：静态图
nn模块(nn module)
PyTorch：神经网络模块nn
PyTorch：优化模块optim
PyTorch：定制神经网络nn模块
PyTorch：控制流和参数共享

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

Such as:

• VSCode withMicrosoft Python extension
• Spyder withAnaconda
• PyCharm