- 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
- 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
- A brief summary of the PTDC ’18 PyTorch 1.0 Preview and Promise - Hacker Noon.pdf
- Automatic differentiation in PyTorch.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_Quick_Start_Guide.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_tutorial_0.0.4_余霆嵩.pdf
- PyTorch_tutorial_0.0.5_余霆嵩.pdf
- pytorch-internals.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
- 深度学习框架PyTorch：入门与实践 - 陈云.pdf
- 深度学习入门之PyTorch - 廖星宇（有目录）.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
- 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
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什么是PyTorch？（What is PyTorch?）
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Autograd：自动求导
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神经网络（Neural Networks）
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训练分类器（Training a Classifier）
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选读：数据并行处理（Optional: Data Parallelism）
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张量(Tensors)
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自动求导(Autograd)
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nn模块(nnmodule)
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