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pytorch tutorials examples and books

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PyTorch tutorials, examples and books

Table of Contents / Contents:

PyTorch tutorials, examples and books
- Table of Contents / Contents:
- PyTorch 1.x tutorials and examples
- Books and slides about PyTorch books, PPTs, etc.
- Here are some independent tutorials
  - 1) PyTorch deep learning: 60 minutes of introduction and practical combat
  - 2) Learning PyTorch with Examples Learning PyTorch
- How to run? Recommended running method

PyTorch 1.x tutorials and examples

0.PyTorch version changes and migration guide
1.PyTorch_for_Numpy_Users PyTorch Guide for Numpy Users
2.PyTorch_Basics PyTorch Basics
3.Linear_Regression Linear Regression
4.Logistic_Regression Logistic Regression
5.Optimizer Optimizer
6.Neural_Network Neural Network
7.Convolutional_Neural_Network(CNN) Convolutional Neural Network
8.Famous_CNN Classic CNN Network
9.Using_Pretrained_models Using pre-trained models
10.Dataset_and_Dataloader Custom data reading
11.Custom_Dataset_example Define your own dataset
12.Visdom_Visualization visdom visualization
13.Tensorboard_Visualization tensorboard visualization
14. Semantic_Segmentation Semantic segmentation
15.Transfer_Learning Transfer Learning
16.Neural_Style(StyleTransfer) Style Transfer
A. Computer Vision and PyTorch
- A brief summary of PyTorch and computer vision
  - Markdown version
  - Notebook version
B.PyTorch Overview

Books and slides about PyTorch books, PPTs, etc.

Note: some of these are old version; The following book part is not version 1.x.

There may be delays in updating this directory. Please refer to the files in this folder for all information.

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 - directory version.pdf
PyTorch 0.4 Chinese Documentation - Translation.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_Yu Tingsong.pdf
PyTorch_tutorial_0.0.5_Yu Tingsong.pdf
pytorch convolution, deconvolution - download from internet.pdf
PyTorch deep learning practice-Hou Yijun.epub
PyTorch deep learning practice-Hou Yijun.pdf
Deep Learning Pytorch - Liao Xingyu.pdf
Deep Learning PyTorch Practical Computer Vision-Tang Jinmin.pdf
PyTorch for Beginners in Deep Learning - Liao Xingyu (with catalog).pdf
Deep Learning Framework PyTorch: Getting Started and Practice - Chen Yun.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-dimensional-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

Here are some independent tutorials

1) PyTorch deep learning: 60 minutes of introduction and practical combat

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What is PyTorch? (What is PyTorch?)

getting Started
Tensor
Operation


NumPy Bridge
Convert torch's Tensor to NumPy array
Convert NumPy array to Torch tensor


Tensors on CUDA



Autograd: Automatically search for guidance

Tensor
gradient



Neural Networks

Define the network
Loss function
Backpropagation
Update weights



Training a Classifier

Where is the data?
Training a picture classifier
1. Load and standardize CIFAR10
2. Define convolutional neural network
3. Define loss functions and optimizers
4. Training the network
5. Use test data to test the network


Training on GPU
Training on multiple GPUs
What to do next?



Optional: Data Parallelism

Import and Parameters
Virtual dataset
Simple model
Create a model and data in parallel
Run the model
result
2 GPUs
3 GPUs
8 GPUs


Summarize

2) Learning PyTorch with Examples Learning PyTorch

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Tensors

Warm up: Use NumPy
PyTorch: Tensors



Automatic derivation (Autograd)

PyTorch: Automatic Derivation (Autograd)
PyTorch: Define your own automatic derivative function
TensorFlow: Static graph



nn module ( nn module)

PyTorch: Neural Network Module nn
PyTorch: Optimize module optim
PyTorch: Customized neural network nn module
PyTorch: Control flow and parameter sharing

How to run? Recommended running method

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 recommended.

Such as: