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pytorch-tutorials-examples-and-books

PyTorch tutorials, examples and some books I found 【不定期更新】整理的PyTorch 最新版教程、例子和书籍

TutorialsTool-specific tutorialsML/AI fundamentalsJupyter Notebook
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创建于 2018-11-10 · 更新于 2026-10-13 · 今日第 10210 名
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bat67

bat67/pytorch-tutorials-examples-and-books 排名第 10,210，有 1,478 个星标。

同类别比较

bat67/pytorch-tutorials-examples-and-books 排名第 10,210，此处有 8 个同类条目；ericdouglas/traduz-ai 排名第 10,215。

同类别比较
名称排名指标
google-deepmind/educational10,168
语言:
Jupyter Notebook
星标数:
1,495
分叉数:
204
ahangchen/GDLnotes10,176
语言:
Python
星标数:
1,488
分叉数:
465
简介:
Google Deep Learning Notes（TensorFlow教程）
tjwei/GANotebooks10,183
语言:
Jupyter Notebook
星标数:
1,487
分叉数:
362
简介:
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
pkmital/CADL10,192
语言:
Jupyter Notebook
星标数:
1,484
分叉数:
714
简介:
ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
ericdouglas/traduz-ai10,215
星标数:
1,477
分叉数:
292
简介:
:pencil: Coleção de artigos de desenvolvimento de software traduzidos para pt-br
GeostatsGuy/PythonNumericalDemos10,223
语言:
Jupyter Notebook
星标数:
1,474
分叉数:
498
简介:
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
elicit/machine-learning-list10,230
星标数:
1,473
分叉数:
125
简介:
A curriculum for learning about foundation models, from scratch to the frontier
m2dsupsdlclass/lectures-labs10,231
语言:
Jupyter Notebook
星标数:
1,473
分叉数:
662
简介:
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别Tool-specific tutorials,ML/AI fundamentals
星标数1,478
分叉数310
贡献者1
创建日期2018-11-10
更新日期2026-10-13
1 天星标数0
7 天星标数-1
排名10,210
上次排名10,225
排名变化15
7 天星标排名17,134
上次 7 天星标排名17,129
7 天星标排名变化-5
总星标排名8,183
上次总星标排名8,172
总星标排名变化-11
README 摘录

PyTorch tutorials, examples and books Table of Contents / 目录: 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 How to run? PyTorch_for_Numpy_Users 给Numpy用户的PyTorch指南 2. Convolutional_Neural_Network(CNN) 卷积神经网络%20卷积神经网络) 8. Tensorboard

_Visualization tensorboard可视化 14. Neural_Style(StyleTransfer) 风格迁移%20风格迁移) A. 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. 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 pytorch卷积

、反卷积 - download from internet. org 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 展开查看 How to run? 推荐的运行方式 Some code in this repo is separated in blocks using .

常见问题

记录了多少星标和分叉？

bat67/pytorch-tutorials-examples-and-books 有 1,478 个星标和 310 个分叉。

仓库排名是多少？

bat67/pytorch-tutorials-examples-and-books 排名第 10,210。

记录的语言和类别是什么？

bat67/pytorch-tutorials-examples-and-books 有 1,478 个星标，语言为 Jupyter Notebook，类别为 Tutorials。

哪个仓库排名最接近？

bat67/pytorch-tutorials-examples-and-books 与 ericdouglas/traduz-ai 比较，后者排名第 10,215，有 1,477 个星标。

README

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

展开查看

  • 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

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

展开查看

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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