This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.
- Official PyTorch Tutorials
- Deep Learning with PyTorch: a 60-minute blitz
- 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
- Custom C extensions
- Writing your own neural network module that uses numpy and scipy
- Reinforcement (Q-)Learning with PyTorch
- Deep Learning with PyTorch: a 60-minute blitz
- 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
- Simple Examples to Introduce PyTorch
- Mini Tutorials in PyTorch
- Tensor Multiplication, Linear Regresison, Logistic Regression, Neural Network, Modern Neural Network, and Convolutional Neural Network
- Deep Learning for NLP
- Introduction to Torch's Tensor Library
- Computation Graphs and Automatic Differentiation
- Deep Learning Building Blocks: Affine maps, non-linearities, and objectives
- Optimization and Training
- Creating Network Components in Pytorch * Example: Logistic Regression Bag-of-Words text classifier
- Word Embeddings: Encoding Lexical Semantics * Example: N-Gram Language Modeling * Exercise: Continuous Bag-of-Words for learning word embeddings
- Sequence modeling and Long-Short Term Memory Networks * Example: An LSTM for Part-of-Speech Tagging * Exercise: Augmenting the LSTM tagger with character-level features
- Advanced: Making Dynamic Decisions * Example: Bi-LSTM Conditional Random Field for named-entity recognition * Exercise: A new loss function
- Deep Learning Tutorial for Researchers
- PyTorch Basics
- Linear Regression
- Logistic Regression
- Feedforward Neural Network
- Convolutional Neural Network
- Deep Residual Network
- Recurrent Neural Network
- Bidirectional Recurrent Neural Network
- Language Model (RNNLM)
- Image Captioning (CNN-RNN)
- Generative Adversarial Network
- Deep Q-Network and Q-learning (WIP)
- Fully Convolutional Networks implemented with PyTorch
- Wasserstein GAN
- OptNet: Differentiable Optimization as a Layer in Neural Networks
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
- Wide ResNet model in PyTorch
- Task-based End-to-end Model Learning
- An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
- Scaling the Scattering Transform: Deep Hybrid Networks
- Adversarial Generator-Encoder Network
- Conditional Similarity Networks
- Multi-style Generative Network for Real-time Transfer
- Image-to-Image Translation with Conditional Adversarial Networks
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Inferring and Executing Programs for Visual Reasoning
- On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
- Train longer, generalize better: closing the generalization gap in large batch training of neural networks
- Neural Message Passing for Quantum Chemistry
- DiracNets: Training Very Deep Neural Networks Without Skip-Connections
- Deal or No Deal? End-to-End Learning for Negotiation Dialogues
- Visual Question Answering in Pytorch
- Principled Detection of Out-of-Distribution Examples in Neural Networks
- Attention is all you need
- FreezeOut: Accelerate Training by Progressively Freezing Layers
- CortexNet: a Generic Network Family for Robust Visual Temporal Representations
- VSE++: Improved Visual-Semantic Embeddings
- Reading Wikipedia to Answer Open-Domain Questions
- A Structured Self-Attentive Sentence Embedding
- Efficient Densenet
- Averaged Stochastic Gradient Descent with Weight Dropped LSTM
- Oriented Response Networks
- Learning to learn by gradient descent by gradient descent
- 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.
- Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- FlowNet: Learning Optical Flow with Convolutional Networks
- Asynchronous Methods for Deep Reinforcement Learning
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- Highway Networks
- Collection of Generative Models with PyTorch
- Generative Adversarial Nets (GAN)
- Variational Autoencoder (VAE)
- A Recurrent Latent Variable Model for Sequential Data (VRNN)
- Hybrid computing using a neural network with dynamic external memory
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- Value Iteration Networks
- YOLOv2: Real-Time Object Detection
- Convolutional Neural Fabrics
- Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Deformable Convolutional Network
- Continuous Deep Q-Learning with Model-based Acceleration
- Hierarchical Attention Networks for Document Classification
- Spatial Transformer Networks
- Decoupled Neural Interfaces using Synthetic Gradients
- Improved Training of Wasserstein GANs
- CycleGAN and Semi-Supervised GAN
- Automatic chemical design using a data-driven continuous representation of molecules
- Differentiable Neural Computer
- Asynchronous Methods for Deep Reinforcement Learning for Atari 2600
- Trust Region Policy Optimization
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- Improving Variational Auto-Encoders using Householder Flow and using convex combination linear Inverse Autoregressive Flow
- SSD: Single Shot MultiBox Detector
- Neural Combinatorial Optimization with Reinforcement Learning
- Pruning Convolutional Neural Networks for Resource Efficient Inference
- A Neural Representation of Sketch Drawings
- Convolutional LSTM Network
- Noisy Networks for Exploration
- Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
- Distributed Proximal Policy Optimization
- Single Shot MultiBox Detector
- Deformable Convolutional Networks in PyTorch
- Dilated ResNet combination with Dilated Convolutions
- Collection of Sequence to Sequence Models with PyTorch
- Vanilla Sequence to Sequence models
- Attention based Sequence to Sequence models
- Faster attention mechanisms using dot products between the final encoder and decoder hidden states
- Reinforcement learning models in ViZDoom environment with PyTorch
- Neuraltalk 2, Image Captioning Model, in PyTorch
- Recurrent Neural Networks for Sentiment Analysis (Aspect-Based) on SemEval 2014
- PyTorch Image Classification with Kaggle Dogs vs Cats Dataset
- CNN Based Text Classification
- Open-source (MIT) Neural Machine Translation (NMT) System
- Pytorch Poetry Generation
- Data Augmentation and Sampling for Pytorch
- CIFAR-10 on Pytorch with VGG, ResNet and DenseNet
- Generate captions from an image with PyTorch
- Generative Adversarial Networks, focusing on anime face drawing
- Simple Generative Adversarial Networks
- Fast Neural Style Transfer
- Pixel-wise Segmentation on VOC2012 Dataset using PyTorch
- Draw like Bob Ross
- Reinforcement learning models using Gym and Pytorch
- Open-Source Neural Machine Translation in PyTorch
- Deep Video Analytics
- Adversarial Auto-encoders
- Whale Detector
- Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
- Open Source Chatbot with PyTorch
- Seq2Seq Intent Parsing
- OpenFace in PyTorch
- Complete Suite for Training Seq2Seq Models in PyTorch
- Probabilistic Programming and Statistical Inference in PyTorch
- Graphics code generating model using Processing
- Load Audio files directly into PyTorch Tensors
- Weight Initializations
- Spatial transformer implemented in PyTorch
- PyTorch AWS AMI, run PyTorch with GPU support in less than 5 minutes
- Use tensorboard with PyTorch
- Simple Fit Module in PyTorch, similar to Keras
##Community
- PyTorch Discussion Forum
- This is actively maintained by Adam Paszke
- StackOverflow PyTorch Tags
- Gloqo
- Add, discover and discuss paper implementations in PyTorch and other frameworks.
Do feel free to contribute!
You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.