PyTorch & Keras Tutorials

Learning PyTorch

• 01 - Deep Learning with PyTorch: A 60 Minute Blitz
  

  • What is PyTorch?
  • Autograd
  • Neural Networks
  • Training a Classifier

• 02 - Learning PyTorch with Examples
  

  • Tensors, Autograd, and nn Module

• 03 - What is torch.nn really?
  

  • Neural Net from Scratch
  • Using torch.nn.functional

• 04 - Visualizing Models, Data, and Training with TensorBoard
  

  • TensorBoard Tutorial

• 05 - Writing Custom Datasets, DataLoaders and Transforms
  

  • Custom DataLoaders and Transforms

Learning PyTorch Lightning

• 01 - Introduction to Pytorch Lightning
  
  • Hello World with MNIST
  • PyTorch Lightning DataModules
  • CIFAR10 ~94% Acc Baseline Tutorial
  • Basic GAN Tutorial

Image/Video

• 01 - Torchvision Object Detection Finetuning Tutorial
  

  • Finetuning Mask R-CNN in the PennFudan Database for Pedestrian Detection and Segmentation

• 02 - Transfer Learning for Computer Vision
  

  • Train a CNN for Image Classification using Transfer Learning
  • Quantized Transfer Learning for Computer Vision Tutorial

• 03 - Adversarial Example Generation
  

  • FPSM: Explaining and Harnessing Adversarial Examples

• 04 - Generative Adversarial Nets
  

  • GANs in PyTorch
  • GANs in Keras
  • DCGAN Tutorial
  • Conditional GAN in Keras
  • Wasserstein GAN in PyTorch
  • Video Generation with TGAN
  • ProteinGAN: Expanding Functional Protein Sequence Space Using Generative Adversarial Networks
  • SN-GAN: Spectrally Normalized Generative Adversarial Networks
  • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
  • Pix2PixHD: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
  • SRGAN: Super-Resolution GAN
  • GauGAN: Semantic Image Synthesis with Spatially-Adaptive Normalization

• 06 - Domain Adaptation Tutorial
  

  • Introduction to Few-Shot Learning and Meta Learning
  • Siamese Networks Tutorial (PyTorch)
  • Siamese Networks Tutorial (Keras)

• 07 - Advances in Few-Shot Learning
  

  • Advances in Few-Shot Learning: A Guided Tour
  • Advances in Few-Shot Learning: Reproducing Results in PyTorch

• 08 - Meta-Learning Models
  

  • Sine Wave Regression with MAML and Reptile
  • Few-Shot Learning with Reptile in Keras
  • Reptile with PyTorch

• 09 - Modules for Convolutional Neural Nets
  

  • SENet: Squeeze-and-Excitation Networks

Text

• 01 - Sequence-to-Sequence Modeling with nn.Transformer and TorchText

• 02 - NLP From Scratch: Classifying Names with a Character-Level RNN

• 03 - NLP From Scratch: Generating Names with a Character-Level RNN

• 04 - NLP From Scratch: Translation with a Sequence to Sequence Network and Attention

• 05 - Text Classification with TorchText

• 06 - Language Translation with TorchText

• 07 - Transformers
  

  • Transformer Tutorial

Audio

• 01 - TorchAudio Tutorial

Reinforcement Learning

• 01 - Reinforcement Learning (DQN) Tutorial

Model Optimization

• 01 - Pruning Tutorial

• 02 - (Experimental) Dynamic Quantization on an LSTM Word Language Model

• 03 - (Experimental) Dynamic Quantization on BERT

• 04 - (Experimental) Static Quantization with Eager Mode in PyTorch

• 05 - (Experimental) Quantized Transfer Learning for Computer Vision Tutorial

Parallel and Distributed Training

• 01 - Single-Machine Model Parallel Best Practices

• 02 - Getting Started with Distributed Data Parallel

• 03 - Writing Distributed Applications with PyTorch

• 04 - Getting Started with Distributed RPC Framework

• 05 - (Advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS

• 06 - Implementing a Parameter Server Using Distributed RPC Framework