The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pull request to contribute to this list.

- Table Of Contents
- Tutorials
- Large Language Models (LLMs)
- Tabular Data
- Visualization
- Explainability
- Object Detection
- Long-Tailed / Out-of-Distribution Recognition
- Activation Functions
- Energy-Based Learning
- Missing Data
- Architecture Search
- Continual Learning
- Optimization
- Quantization
- Quantum Machine Learning
- Neural Network Compression
- Facial, Action and Pose Recognition
- Super resolution
- Synthetesizing Views
- Voice
- Medical
- 3D Segmentation, Classification and Regression
- Video Recognition
- Recurrent Neural Networks (RNNs)
- Convolutional Neural Networks (CNNs)
- Segmentation
- Geometric Deep Learning: Graph \& Irregular Structures
- Sorting
- Ordinary Differential Equations Networks
- Multi-task Learning
- GANs, VAEs, and AEs
- Unsupervised Learning
- Adversarial Attacks
- Style Transfer
- Image Captioning
- Transformers
- Similarity Networks and Functions
- Reasoning
- General NLP
- Question and Answering
- Speech Generation and Recognition
- Document and Text Classification
- Text Generation
- Text to Image
- Translation
- Sentiment Analysis
- Deep Reinforcement Learning
- Deep Bayesian Learning and Probabilistic Programmming
- Spiking Neural Networks
- Anomaly Detection
- Regression Types
- Time Series
- Synthetic Datasets
- Neural Network General Improvements
- DNN Applications in Chemistry and Physics
- New Thinking on General Neural Network Architecture
- Linear Algebra
- API Abstraction
- Low Level Utilities
- PyTorch Utilities
- PyTorch Video Tutorials
- Community
- To be Classified
- Links to This Repository
- Contributions

- Official PyTorch Tutorials
- Official PyTorch Examples
- Dive Into Deep Learning with PyTorch
- Minicourse in Deep Learning with PyTorch (Multi-language)
- Practical Deep Learning with PyTorch
- Deep Learning Models
- C++ Implementation of PyTorch Tutorial
- Simple Examples to Introduce PyTorch
- Mini Tutorials in PyTorch
- Deep Learning for NLP
- Deep Learning Tutorial for Researchers
- Fully Convolutional Networks implemented with PyTorch
- Simple PyTorch Tutorials Zero to ALL
- DeepNLP-models-Pytorch
- MILA PyTorch Welcome Tutorials
- Effective PyTorch, Optimizing Runtime with TorchScript and Numerical Stability Optimization
- Practical PyTorch
- PyTorch Project Template
- Semantic Search with PyTorch

- LLM Tutorials
- General
- GPT Fast, fast and hackable pytorch native transformer inference
- Mixtral Offloading, run Mixtral-8x7B models in Colab or consumer desktops
- Llama
- Llama Recipes
- TinyLlama
- Mosaic Pretrained Transformers (MPT)
- VLLM, high-throughput and memory-efficient inference and serving engine for LLMs
- Dolly
- Vicuna
- Mistral 7B
- BigDL LLM, library for running LLM (large language model) on Intel XPU (from Laptop to GPU to Cloud) using INT4 with very low latency1 (for any PyTorch model)
- Simple LLM Finetuner
- Petals, run LLMs at home, BitTorrent-style, fine-tuning and inference up to 10x faster than offloading

- Japanese
- Chinese
- Retrieval Augmented Generation (RAG)
- Embeddings
- Applications
- Finetuning
- Training
- Quantization

- Pytorch Tabular,standard framework for modelling Deep Learning Models for tabular data
- Tab Transformer
- PyTorch-TabNet: Attentive Interpretable Tabular Learning
- carefree-learn: A minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch

- Loss Visualization
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- SmoothGrad: removing noise by adding noise
- DeepDream: dream-like hallucinogenic visuals
- FlashTorch: Visualization toolkit for neural networks in PyTorch
- Lucent: Lucid adapted for PyTorch
- DreamCreator: Training GoogleNet models for DeepDream with custom datasets made simple
- CNN Feature Map Visualisation

- Neural-Backed Decision Trees
- Efficient Covariance Estimation from Temporal Data
- Hierarchical interpretations for neural network predictions
- Shap, a unified approach to explain the output of any machine learning model
- VIsualizing PyTorch saved .pth deep learning models with netron
- Distilling a Neural Network Into a Soft Decision Tree
- Captum, A unified model interpretability library for PyTorch

- MMDetection Object Detection Toolbox
- Mask R-CNN Benchmark: Faster R-CNN and Mask R-CNN in PyTorch 1.0
- YOLOS
- YOLOF
- YOLOX
- Yolov7
- YOLOv6
- Yolov5
- Yolov4
- YOLOv3
- YOLOv2: Real-Time Object Detection
- SSD: Single Shot MultiBox Detector
- Detectron models for Object Detection
- Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
- Whale Detector
- Catalyst.Detection

- Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
- Invariant Risk Minimization
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples
- Deep Anomaly Detection with Outlier Exposure
- Large-Scale Long-Tailed Recognition in an Open World
- Principled Detection of Out-of-Distribution Examples in Neural Networks
- Learning Confidence for Out-of-Distribution Detection in Neural Networks
- PyTorch Imbalanced Class Sampler

- EfficientNetV2
- DenseNAS
- DARTS: Differentiable Architecture Search
- Efficient Neural Architecture Search (ENAS)
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

- AccSGD, AdaBound, AdaMod, DiffGrad, Lamb, NovoGrad, RAdam, SGDW, Yogi and more
- Lookahead Optimizer: k steps forward, 1 step back
- RAdam, On the Variance of the Adaptive Learning Rate and Beyond
- Over9000, Comparison of RAdam, Lookahead, Novograd, and combinations
- AdaBound, Train As Fast as Adam As Good as SGD
- Riemannian Adaptive Optimization Methods
- L-BFGS
- OptNet: Differentiable Optimization as a Layer in Neural Networks
- Learning to learn by gradient descent by gradient descent
- Surrogate Gradient Learning in Spiking Neural Networks
- TorchOpt: An Efficient Library for Differentiable Optimization

- Tor10, generic tensor-network library for quantum simulation in PyTorch
- PennyLane, cross-platform Python library for quantum machine learning with PyTorch interface

- Bayesian Compression for Deep Learning
- Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research
- Learning Sparse Neural Networks through L0 regularization
- Energy-constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
- EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
- Pruning Convolutional Neural Networks for Resource Efficient Inference
- Pruning neural networks: is it time to nip it in the bud? (showing reduced networks work better)

- Facenet: Pretrained Pytorch face detection and recognition models
- DGC-Net: Dense Geometric Correspondence Network
- High performance facial recognition library on PyTorch
- FaceBoxes, a CPU real-time face detector with high accuracy
- How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
- Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
- PyTorch Realtime Multi-Person Pose Estimation
- SphereFace: Deep Hypersphere Embedding for Face Recognition
- GANimation: Anatomically-aware Facial Animation from a Single Image
- Shufflenet V2 by Face++ with better results than paper
- Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
- Unsupervised Learning of Depth and Ego-Motion from Video
- FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- FlowNet: Learning Optical Flow with Convolutional Networks
- Optical Flow Estimation using a Spatial Pyramid Network
- OpenFace in PyTorch
- Deep Face Recognition in PyTorch

- Enhanced Deep Residual Networks for Single Image Super-Resolution
- Superresolution using an efficient sub-pixel convolutional neural network
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution

- Medical Zoo, 3D multi-modal medical image segmentation library in PyTorch
- U-Net for FLAIR Abnormality Segmentation in Brain MRI
- Genomic Classification via ULMFiT
- Deep Neural Networks Improve Radiologistsâ€™ Performance in Breast Cancer Screening
- Delira, lightweight framework for medical imaging prototyping
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- Medical Torch, medical imaging framework for PyTorch
- TorchXRayVision - A library for chest X-ray datasets and models. Including pre-trainined models.

- Kaolin, Library for Accelerating 3D Deep Learning Research
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 3D segmentation with MONAI and Catalyst

- Dancing to Music
- Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations
- Deep Video Analytics
- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

- SRU: training RNNs as fast as CNNs
- Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
- Averaged Stochastic Gradient Descent with Weight Dropped LSTM
- Training RNNs as Fast as CNNs
- Quasi-Recurrent Neural Network (QRNN)
- ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
- A Recurrent Latent Variable Model for Sequential Data (VRNN)
- Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
- Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
- Attentive Recurrent Comparators
- 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

- LegoNet: Efficient Convolutional Neural Networks with Lego Filters
- MeshCNN, a convolutional neural network designed specifically for triangular meshes
- Octave Convolution
- PyTorch Image Models, ResNet/ResNeXT, DPN, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet
- Deep Neural Networks with Box Convolutions
- Invertible Residual Networks
- Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
- Faster Faster R-CNN Implementation
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
- Wide ResNet model in PyTorch -DiracNets: Training Very Deep Neural Networks Without Skip-Connections
- An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
- Efficient Densenet
- Video Frame Interpolation via Adaptive Separable Convolution
- Learning local feature descriptors with triplets and shallow convolutional neural networks
- Densely Connected Convolutional Networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- Deep Residual Learning for Image Recognition
- Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch
- Deformable Convolutional Network
- Convolutional Neural Fabrics
- Deformable Convolutional Networks in PyTorch
- Dilated ResNet combination with Dilated Convolutions
- Striving for Simplicity: The All Convolutional Net
- Convolutional LSTM Network
- Big collection of pretrained classification models
- PyTorch Image Classification with Kaggle Dogs vs Cats Dataset
- CIFAR-10 on Pytorch with VGG, ResNet and DenseNet
- Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
- NVIDIA/unsupervised-video-interpolation

- Detectron2 by FAIR
- Pixel-wise Segmentation on VOC2012 Dataset using PyTorch
- Pywick - High-level batteries-included neural network training library for Pytorch
- Improving Semantic Segmentation via Video Propagation and Label Relaxation
- Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
- Catalyst.Segmentation
- Segmentation models with pretrained backbones

- PyTorch Geometric, Deep Learning Extension
- PyTorch Geometric Temporal: A Temporal Extension Library for PyTorch Geometric
- PyTorch Geometric Signed Directed: A Signed & Directed Extension Library for PyTorch Geometric
- ChemicalX: A PyTorch Based Deep Learning Library for Drug Pair Scoring
- Self-Attention Graph Pooling
- Position-aware Graph Neural Networks
- Signed Graph Convolutional Neural Network
- Graph U-Nets
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
- Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
- PyTorch BigGraph by FAIR for Generating Embeddings From Large-scale Graph Data
- Capsule Graph Neural Network
- Splitter: Learning Node Representations that Capture Multiple Social Contexts
- A Higher-Order Graph Convolutional Layer
- Predict then Propagate: Graph Neural Networks meet Personalized PageRank
- Lorentz Embeddings: Learn Continuous Hierarchies in Hyperbolic Space
- Graph Wavelet Neural Network
- Watch Your Step: Learning Node Embeddings via Graph Attention
- Signed Graph Convolutional Network
- Graph Classification Using Structural Attention
- SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
- SINE: Scalable Incomplete Network Embedding
- HypER: Hypernetwork Knowledge Graph Embeddings
- TuckER: Tensor Factorization for Knowledge Graph Completion
- PyKEEN: A Python library for learning and evaluating knowledge graph embeddings
- Pathfinder Discovery Networks for Neural Message Passing
- SSSNET: Semi-Supervised Signed Network Clustering
- MagNet: A Neural Network for Directed Graphs

- Latent ODEs for Irregularly-Sampled Time Series
- GRU-ODE-Bayes: continuous modelling of sporadically-observed time series

- Hierarchical Multi-Task Learning Model
- Task-based End-to-end Model Learning
- torchMTL: A lightweight module for Multi-Task Learning in pytorch

- BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
- High Fidelity Performance Metrics for Generative Models in PyTorch
- Mimicry, PyTorch Library for Reproducibility of GAN Research
- Clean Readable CycleGAN
- StarGAN
- Block Neural Autoregressive Flow
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- A Style-Based Generator Architecture for Generative Adversarial Networks
- GANDissect, PyTorch Tool for Visualizing Neurons in GANs
- Learning deep representations by mutual information estimation and maximization
- Variational Laplace Autoencoders
- VeGANS, library for easily training GANs
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Conditional GAN
- Wasserstein GAN
- Adversarial Generator-Encoder Network
- Image-to-Image Translation with Conditional Adversarial Networks
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
- Improved Training of Wasserstein GANs
- Collection of Generative Models with PyTorch
- Generative Adversarial Nets (GAN)
- Variational Autoencoder (VAE)

- Improved Training of Wasserstein GANs
- CycleGAN and Semi-Supervised GAN
- Improving Variational Auto-Encoders using Householder Flow and using convex combination linear Inverse Autoregressive Flow
- PyTorch GAN Collection
- Generative Adversarial Networks, focusing on anime face drawing
- Simple Generative Adversarial Networks
- Adversarial Auto-encoders
- torchgan: Framework for modelling Generative Adversarial Networks in Pytorch
- Evaluating Lossy Compression Rates of Deep Generative Models
- Catalyst.GAN

- Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
- AND: Anchor Neighbourhood Discovery

- Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
- Explaining and Harnessing Adversarial Examples
- AdverTorch - A Toolbox for Adversarial Robustness Research

- Pystiche: Framework for Neural Style Transfer
- Detecting Adversarial Examples via Neural Fingerprinting
- A Neural Algorithm of Artistic Style
- Multi-style Generative Network for Real-time Transfer
- DeOldify, Coloring Old Images
- Neural Style Transfer
- Fast Neural Style Transfer
- Draw like Bob Ross

- CLIP (Contrastive Language-Image Pre-Training)
- Neuraltalk 2, Image Captioning Model, in PyTorch
- Generate captions from an image with PyTorch
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning

- nanoGPT, fastest repository for training/finetuning medium-sized GPTs
- minGPT, Re-implementation of GPT to be small, clean, interpretable and educational
- Espresso, Module Neural Automatic Speech Recognition Toolkit
- Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
- XLNet
- Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
- Cross-lingual Language Model Pretraining
- Libre Office Translate via PyTorch NMT
- BERT
- VSE++: Improved Visual-Semantic Embeddings
- A Structured Self-Attentive Sentence Embedding
- Neural Sequence labeling model
- Skip-Thought Vectors
- Complete Suite for Training Seq2Seq Models in PyTorch
- MUSE: Multilingual Unsupervised and Supervised Embeddings
- TorchMoji: PyTorch Implementation of DeepMoji to under Language used to Express Emotions

- Visual Question Answering in Pytorch
- Reading Wikipedia to Answer Open-Domain Questions
- Deal or No Deal? End-to-End Learning for Negotiation Dialogues
- Interpretable Counting for Visual Question Answering
- Open Source Chatbot with PyTorch

- PyTorch-Kaldi Speech Recognition Toolkit
- WaveGlow: A Flow-based Generative Network for Speech Synthesis
- OpenNMT
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit

- Hierarchical Attention Network for Document Classification
- Hierarchical Attention Networks for Document Classification
- CNN Based Text Classification

- Recurrent Neural Networks for Sentiment Analysis (Aspect-Based) on SemEval 2014
- Seq2Seq Intent Parsing
- Finetuning BERT for Sentiment Analysis

- Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
- Exploration by Random Network Distillation
- EGG: Emergence of lanGuage in Games, quickly implement multi-agent games with discrete channel communication
- Temporal Difference VAE
- High-performance Atari A3C Agent in 180 Lines PyTorch
- Learning when to communicate at scale in multiagent cooperative and competitive tasks
- Actor-Attention-Critic for Multi-Agent Reinforcement Learning
- PPO in PyTorch C++
- Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
- Asynchronous Methods for Deep Reinforcement Learning
- Continuous Deep Q-Learning with Model-based Acceleration
- Asynchronous Methods for Deep Reinforcement Learning for Atari 2600
- Trust Region Policy Optimization
- Neural Combinatorial Optimization with Reinforcement Learning
- Noisy Networks for Exploration
- Distributed Proximal Policy Optimization
- Reinforcement learning models in ViZDoom environment with PyTorch
- Reinforcement learning models using Gym and Pytorch
- SLM-Lab: Modular Deep Reinforcement Learning framework in PyTorch
- Catalyst.RL

- BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
- Subspace Inference for Bayesian Deep Learning
- Bayesian Deep Learning with Variational Inference Package
- Probabilistic Programming and Statistical Inference in PyTorch
- Bayesian CNN with Variational Inferece in PyTorch

- Dual Self-Attention Network for Multivariate Time Series Forecasting
- DILATE: DIstortion Loss with shApe and tImE
- Variational Recurrent Autoencoder for Timeseries Clustering
- Spatio-Temporal Neural Networks for Space-Time Series Modeling and Relations Discovery
- Flow Forecast: A deep learning for time series forecasting framework built in PyTorch

- In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
- Train longer, generalize better: closing the generalization gap in large batch training of neural networks
- FreezeOut: Accelerate Training by Progressively Freezing Layers
- Binary Stochastic Neurons
- Compact Bilinear Pooling
- Mixed Precision Training in PyTorch

- Wave Physics as an Analog Recurrent Neural Network
- Neural Message Passing for Quantum Chemistry
- Automatic chemical design using a data-driven continuous representation of molecules
- Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge
- Differentiable Molecular Simulation for Learning and Control

- Torch Layers, Shape inference for PyTorch, SOTA Layers
- Hummingbird, run trained scikit-learn models on GPU with PyTorch

- Functorch: prototype of JAX-like composable Function transformers for PyTorch
- Poutyne: Simplified Framework for Training Neural Networks
- PyTorch Metric Learning
- Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
- BackPACK to easily Extract Variance, Diagonal of Gauss-Newton, and KFAC
- PyHessian for Computing Hessian Eigenvalues, trace of matrix, and ESD
- Hessian in PyTorch
- Differentiable Convex Layers
- Albumentations: Fast Image Augmentation Library
- Higher, obtain higher order gradients over losses spanning training loops
- Neural Pipeline, Training Pipeline for PyTorch
- Layer-by-layer PyTorch Model Profiler for Checking Model Time Consumption
- Sparse Distributions
- Diffdist, Adds Support for Differentiable Communication allowing distributed model parallelism
- HessianFlow, Library for Hessian Based Algorithms
- Texar, PyTorch Toolkit for Text Generation
- PyTorch FLOPs counter
- PyTorch Inference on C++ in Windows
- EuclidesDB, Multi-Model Machine Learning Feature Database
- Data Augmentation and Sampling for Pytorch
- PyText, deep learning based NLP modelling framework officially maintained by FAIR
- Torchstat for Statistics on PyTorch Models
- 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
- torchbearer: A model fitting library for PyTorch
- PyTorch to Keras model converter
- Gluon to PyTorch model converter with code generation
- Catalyst: High-level utils for PyTorch DL & RL research
- PyTorch Lightning: Scalable and lightweight deep learning research framework
- Determined: Scalable deep learning platform with PyTorch support
- PyTorch-Ignite: High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently
- torchvision: A package consisting of popular datasets, model architectures, and common image transformations for computer vision.
- Poutyne: A Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.
- torchensemble: Scikit-Learn like ensemble methods in PyTorch

- PyTorch Zero to All Lectures
- PyTorch For Deep Learning Full Course
- PyTorch Lightning 101 with Alfredo Canziani and William Falcon
- Practical Deep Learning with PyTorch

- Perturbative Neural Networks
- Accurate Neural Network Potential
- Scaling the Scattering Transform: Deep Hybrid Networks
- CortexNet: a Generic Network Family for Robust Visual Temporal Representations
- Oriented Response Networks
- Associative Compression Networks
- Clarinet
- Continuous Wavelet Transforms
- mixup: Beyond Empirical Risk Minimization
- Network In Network
- Highway Networks
- Hybrid computing using a neural network with dynamic external memory
- Value Iteration Networks
- Differentiable Neural Computer
- A Neural Representation of Sketch Drawings
- Understanding Deep Image Representations by Inverting Them
- NIMA: Neural Image Assessment
- NASNet-A-Mobile. Ported weights
- Graphics code generating model using Processing

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 or create a new section if itâ€™s a new category.