PyTorch tutorials, examples and books

Table of Contents / 目录:

PyTorch tutorials, examples and books

PyTorch 1.x tutorials and examples

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

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

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* 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

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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

2) Learning PyTorch with Examples 用例子学习PyTorch

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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

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:

VSCode with Microsoft Python extension
Spyder with Anaconda
PyCharm

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