The Incredible PyTorch

What is this?

This is inspired by the famous Awesome TensorFlow repository where this repository would hold tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.

Tutorials

• Official PyTorch Tutorials
  1. [Deep Learning with PyTorch: a 60-minute blitz](Deep Learning with PyTorch.ipynb)
     • A perfect introduction to PyTorch's torch, autograd, nn and optim APIs
     • If you are a former Torch user, you can check out this instead: [Introduction to PyTorch for former Torchies](Introduction to PyTorch for former Torchies.ipynb)
  2. Custom C extensions
     • Write your own C code that interfaces into PyTorch via FFI
  3. [Writing your own neural network module that uses numpy and scipy](Creating extensions using numpy and scipy.ipynb)
  4. [Reinforcement (Q-)Learning with PyTorch](Reinforcement (Q-)Learning with PyTorch.ipynb)
• Official PyTorch Examples
  • MNIST Convnets
  • Word level Language Modeling using LSTM RNNs
  • Training Imagenet Classifiers with Residual Networks
  • Generative Adversarial Networks (DCGAN)
  • Variational Auto-Encoders
  • Superresolution using an efficient sub-pixel convolutional neural network
  • Hogwild training of shared ConvNets across multiple processes on MNIST
  • Training a CartPole to balance in OpenAI Gym with actor-critic
  • Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext
• Practical PyTorch
  • This focuses on using RNNs for NLP
  • Classifying Names with a Character-Level RNN
  • Generating Names with a Character-Level RNN
  • Translation with a Sequence to Sequence Network and Attention
• Simple Examples to Introduce PyTorch
• Mini Tutorials in PyTorch
  • Tensor Multiplication, Linear Regresison, Logistic Regression, Neural Network, Modern Neural Network, and Convolutional Neural Network

Papers in PyTorch

• Learning to learn by gradient descent by gradient descent
• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
• Wasserstein GAN
• Densely Connected Convolutional Networks
• A Neural Algorithm of Artistic Style
• Very Deep Convolutional Networks for Large-Scale Image Recognition
  • VGG model in PyTorch.
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• Network In Network
• Deep Residual Learning for Image Recognition
  • ResNet model in PyTorch.
• Wide Residual Networks
  • Wide ResNet model in PyTorch
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• FlowNet: Learning Optical Flow with Convolutional Networks

Projects in Pytorch

• Reinforcement learning models in ViZDoom environment with PyTorch
• Collection of Generative Models with PyTorch
  • Generative Adversarial Nets (GAN)
    1. Vanilla GAN
    2. Conditional GAN
    3. InfoGAN
    4. Wasserstein GAN
    5. Mode Regularized GAN
  • Variational Autoencoder (VAE)
    1. Vanilla VAE
    2. Conditional VAE
    3. Denoising VAE
    4. Adversarial Autoencoder
    5. Adversarial Variational Bayes

Community

• PyTorch Discussion Forum
  • This is actively maintained by Adam Paszke
• StackOverFlow PyTorch Tags

Contributions

Do feel free to contribute!

You can raise an isssue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.