[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9694":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":15,"starSnapshotCount":15,"syncStatus":17,"lastSyncTime":35,"discoverSource":36},9694,"the-incredible-pytorch","ritchieng\u002Fthe-incredible-pytorch","ritchieng","The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. ","",null,12551,2214,460,3,0,1,2,32,4,76.2,"MIT License",false,"master",true,[26,27,28,29,30,31],"deep-learning","deep-learning-library","deep-learning-tutorial","deep-neural-networks","python","pytorch","2026-06-12 04:00:46","\u003Cp align=\"center\">\u003Cimg width=\"40%\" src=\"the_incredible_pytorch.png\" \u002F>\u003C\u002Fp>\n\n--------------------------------------------------------------------------------\n\u003Cp align=\"center\">\n\t\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fstars-10000+-blue.svg\"\u002F>\n\t\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fforks-1900+-blue.svg\"\u002F>\n\t\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-MIT-blue.svg\"\u002F>\n\u003C\u002Fp>\n\nThis is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible [PyTorch](http:\u002F\u002Fpytorch.org\u002F). Feel free to make a pull request to contribute to this list.\n\n\n# Table Of Contents\n\u003C!-- vscode-markdown-toc -->\n\n- [Table Of Contents](#table-of-contents)\n - [Tutorials](#tutorials)\n - [Large Language Models (LLMs)](#large-language-models-llms)\n - [Agentic AI](#agentic-ai)\n - [Guardrails and AI Safety](#guardrails-and-ai-safety)\n - [Tabular Data](#tabular-data)\n - [Visualization](#visualization)\n - [Explainability](#explainability)\n - [Object Detection](#object-detection)\n - [Long-Tailed \u002F Out-of-Distribution Recognition](#long-tailed--out-of-distribution-recognition)\n - [Activation Functions](#activation-functions)\n - [Energy-Based Learning](#energy-based-learning)\n - [Missing Data](#missing-data)\n - [Architecture Search](#architecture-search)\n - [Continual Learning](#continual-learning)\n - [Optimization](#optimization)\n - [Quantization](#quantization)\n - [Quantum Machine Learning](#quantum-machine-learning)\n - [Neural Network Compression](#neural-network-compression)\n - [Facial, Action and Pose Recognition](#facial-action-and-pose-recognition)\n - [Super resolution](#super-resolution)\n - [Synthetesizing Views](#synthetesizing-views)\n - [Voice](#voice)\n - [Medical](#medical)\n - [3D Segmentation, Classification and Regression](#3d-segmentation-classification-and-regression)\n - [Video Recognition](#video-recognition)\n - [Recurrent Neural Networks (RNNs)](#recurrent-neural-networks-rnns)\n - [Convolutional Neural Networks (CNNs)](#convolutional-neural-networks-cnns)\n - [Segmentation](#segmentation)\n - [Geometric Deep Learning: Graph \\& Irregular Structures](#geometric-deep-learning-graph--irregular-structures)\n - [Sorting](#sorting)\n - [Ordinary Differential Equations Networks](#ordinary-differential-equations-networks)\n - [Multi-task Learning](#multi-task-learning)\n - [GANs, VAEs, and AEs](#gans-vaes-and-aes)\n - [Unsupervised Learning](#unsupervised-learning)\n - [Adversarial Attacks](#adversarial-attacks)\n - [Style Transfer](#style-transfer)\n - [Image Captioning](#image-captioning)\n - [Transformers](#transformers)\n - [Similarity Networks and Functions](#similarity-networks-and-functions)\n - [Reasoning](#reasoning)\n - [General NLP](#general-nlp)\n - [Question and Answering](#question-and-answering)\n - [Speech Generation and Recognition](#speech-generation-and-recognition)\n - [Document and Text Classification](#document-and-text-classification)\n - [Text Generation](#text-generation)\n - [Text to Image](#text-to-image)\n - [Translation](#translation)\n - [Sentiment Analysis](#sentiment-analysis)\n - [Deep Reinforcement Learning](#deep-reinforcement-learning)\n - [Deep Bayesian Learning and Probabilistic Programmming](#deep-bayesian-learning-and-probabilistic-programmming)\n - [Spiking Neural Networks](#spiking-neural-networks)\n - [Anomaly Detection](#anomaly-detection)\n - [Regression Types](#regression-types)\n - [Time Series](#time-series)\n - [Synthetic Datasets](#synthetic-datasets)\n - [Neural Network General Improvements](#neural-network-general-improvements)\n - [DNN Applications in Chemistry and Physics](#dnn-applications-in-chemistry-and-physics)\n - [New Thinking on General Neural Network Architecture](#new-thinking-on-general-neural-network-architecture)\n - [Linear Algebra](#linear-algebra)\n - [API Abstraction](#api-abstraction)\n - [Low Level Utilities](#low-level-utilities)\n - [PyTorch Utilities](#pytorch-utilities)\n - [PyTorch Video Tutorials](#pytorch-video-tutorials)\n - [Community](#community)\n - [To be Classified](#to-be-classified)\n - [Links to This Repository](#links-to-this-repository)\n - [Contributions](#contributions)\n - [New Special Dedicated List to AI Agents | The Incredible AI Agents](#new-special-dedicated-list-to-ai-agents--the-incredible-ai-agents)\n\n\u003C!-- vscode-markdown-toc-config\n\tnumbering=false\n\tautoSave=true\n\t\u002Fvscode-markdown-toc-config -->\n\u003C!-- \u002Fvscode-markdown-toc -->\n\n## \u003Ca name='Tutorials'>\u003C\u002Fa>Tutorials\n- [Official PyTorch Tutorials](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftutorials)\n- [Official PyTorch Examples](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fexamples)\n- [The Math Behind Artificial Intelligence: A Guide to AI Foundations [Full Book]](https:\u002F\u002Fwww.freecodecamp.org\u002Fnews\u002Fthe-math-behind-artificial-intelligence-book\u002F)\n- [Dive Into Deep Learning with PyTorch](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en)\n- [How to Read Pytorch](https:\u002F\u002Fgithub.com\u002Fdavidbau\u002Fhow-to-read-pytorch)\n- [Minicourse in Deep Learning with PyTorch (Multi-language)](https:\u002F\u002Fgithub.com\u002FAtcold\u002Fpytorch-Deep-Learning-Minicourse)\n- [Practical Deep Learning with PyTorch](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fdeep-learning-wizard)\n- [Deep Learning Models](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fdeeplearning-models)\n- [C++ Implementation of PyTorch Tutorial](https:\u002F\u002Fgithub.com\u002Fprabhuomkar\u002Fpytorch-cpp)\n- [Simple Examples to Introduce PyTorch](https:\u002F\u002Fgithub.com\u002Fjcjohnson\u002Fpytorch-examples)\n- [Mini Tutorials in PyTorch](https:\u002F\u002Fgithub.com\u002Fvinhkhuc\u002FPyTorch-Mini-Tutorials)\n- [Deep Learning for NLP](https:\u002F\u002Fgithub.com\u002Frguthrie3\u002FDeepLearningForNLPInPytorch)\n- [Deep Learning Tutorial for Researchers](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial)\n- [Fully Convolutional Networks implemented with PyTorch](https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Fpytorch-fcn)\n- [Simple PyTorch Tutorials Zero to ALL](https:\u002F\u002Fgithub.com\u002Fhunkim\u002FPyTorchZeroToAll)\n- [DeepNLP-models-Pytorch](https:\u002F\u002Fgithub.com\u002FDSKSD\u002FDeepNLP-models-Pytorch)\n- [MILA PyTorch Welcome Tutorials](https:\u002F\u002Fgithub.com\u002Fmila-udem\u002Fwelcome_tutorials)\n- [Effective PyTorch, Optimizing Runtime with TorchScript and Numerical Stability Optimization](https:\u002F\u002Fgithub.com\u002Fvahidk\u002FEffectivePyTorch)\n- [Practical PyTorch](https:\u002F\u002Fgithub.com\u002Fspro\u002Fpractical-pytorch)\n- [PyTorch Project Template](https:\u002F\u002Fgithub.com\u002Fmoemen95\u002FPyTorch-Project-Template)\n- [Semantic Search with PyTorch](https:\u002F\u002Fgithub.com\u002Fkuutsav\u002Finformation-retrieval)\n\n## \u003Ca name='LargeLanguageModels'>\u003C\u002Fa>Large Language Models (LLMs)\n- LLM Tutorials\n - [Build a Large Language Model (From Scratch)](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch)\n - [Hugginface LLM Training Book, a collection of methodologies to help with successful training of large language models](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fllm_training_handbook)\n- General\n - [Starcoder 2, family of code generation models](https:\u002F\u002Fgithub.com\u002Fbigcode-project\u002Fstarcoder2)\n - [GPT Fast, fast and hackable pytorch native transformer inference](https:\u002F\u002Fgithub.com\u002Fpytorch-labs\u002Fgpt-fast)\n - [Mixtral Offloading, run Mixtral-8x7B models in Colab or consumer desktops](https:\u002F\u002Fgithub.com\u002Fdvmazur\u002Fmixtral-offloading)\n - [Llama](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fllama)\n - [Llama Recipes](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fllama-recipes)\n - [TinyLlama](https:\u002F\u002Fgithub.com\u002Fjzhang38\u002FTinyLlama)\n - [Mosaic Pretrained Transformers (MPT)](https:\u002F\u002Fgithub.com\u002Fmosaicml\u002Fllm-foundry)\n - [VLLM, high-throughput and memory-efficient inference and serving engine for LLMs](https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm)\n - [Dolly](https:\u002F\u002Fgithub.com\u002Fdatabrickslabs\u002Fdolly)\n - [Vicuna](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat)\n - [Mistral 7B](https:\u002F\u002Fgithub.com\u002Fmistralai\u002Fmistral-src)\n - [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)](https:\u002F\u002Fgithub.com\u002Fintel-analytics\u002FBigDL)\n - [Simple LLM Finetuner](https:\u002F\u002Fgithub.com\u002Flxe\u002Fsimple-llm-finetuner)\n - [Petals, run LLMs at home, BitTorrent-style, fine-tuning and inference up to 10x faster than offloading](https:\u002F\u002Fgithub.com\u002Fbigscience-workshop\u002Fpetals)\n - [Gemma, Google's family of lightweight, state-of-the-art open models](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fgemma_pytorch)\n - [Qwen, Alibaba Cloud's large language model](https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen)\n - [CodeT5, code-aware encoder-decoder model for code understanding and generation](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FCodeT5)\n - [OpenLLaMA, permissively licensed open source reproduction of Meta AI's LLaMA](https:\u002F\u002Fgithub.com\u002Fopenlm-research\u002Fopen_llama)\n - [RedPajama, leading open-source models with package to reproduce LLaMA training dataset](https:\u002F\u002Fgithub.com\u002Ftogethercomputer\u002FRedPajama-Data)\n - [MosaicML LLM Foundry, codebase for training, finetuning, and deploying LLMs](https:\u002F\u002Fgithub.com\u002Fmosaicml\u002Fllm-foundry)\n - [TECS-L (Golden MoE), dense-to-MoE conversion framework with optimal inhibition ratio I≈1\u002Fe for PyTorch LLMs](https:\u002F\u002Fgithub.com\u002Fneed-singularity\u002FTECS-L)\n- Japanese\n - [Japanese Llama](https:\u002F\u002Fgithub.com\u002Fmasa3141\u002Fjapanese-alpaca-lora)\n - [Japanese GPT Neox and Open Calm](https:\u002F\u002Fgithub.com\u002FhppRC\u002Fllm-lora-classification)\n- Chinese\n - [Chinese Llamma-2 7B](https:\u002F\u002Fgithub.com\u002FLinkSoul-AI\u002FChinese-Llama-2-7b)\n - [Chinese Vicuna](https:\u002F\u002Fgithub.com\u002FFacico\u002FChinese-Vicuna)\n- Retrieval Augmented Generation (RAG)\n - [LlamaIndex, data framework for your LLM application](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index)\n- Embeddings\n - [ChromaDB, open-source embedding database](https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma)\n- Applications\n - [Langchain, building applications with LLMs through composability](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain)\n - [LangSmith, platform for building production-grade LLM applications](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk)\n - [LiteLLM, call all LLM APIs using the OpenAI format](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm)\n - [OpenAI Python, official Python library for the OpenAI API](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-python)\n - [Guidance, library for controlling large language models](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fguidance)\n- Finetuning\n - [Huggingface PEFT, State-of-the-art Parameter-Efficient Fine-Tuning](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft)\n - [Unsloth, finetune LLMs 2-5x faster with 80% less memory](https:\u002F\u002Fgithub.com\u002Funslothai\u002Funsloth)\n - [LoRA, Low-Rank Adaptation of Large Language Models](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLoRA)\n - [QLoRA, efficient finetuning of quantized LLMs](https:\u002F\u002Fgithub.com\u002Fartidoro\u002Fqlora)\n - [Axolotl, tool designed to streamline the fine-tuning of various AI models](https:\u002F\u002Fgithub.com\u002FOpenAccess-AI-Collective\u002Faxolotl)\n - [LLaMA-Factory, unified efficient fine-tuning of 100+ LLMs](https:\u002F\u002Fgithub.com\u002Fhiyouga\u002FLLaMA-Factory)\n- Training\n - [Higgsfield, Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters](https:\u002F\u002Fgithub.com\u002Fhiggsfield-ai\u002Fhiggsfield)\n - [DeepSpeed, deep learning optimization library](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FDeepSpeed)\n - [FairScale, PyTorch extensions for high performance and large scale training](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffairscale)\n - [Accelerate, simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Faccelerate)\n - [ColossalAI, unified deep learning system for large-scale model training and inference](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI)\n- Quantization\n - [AutoGPTQ, easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm](https:\u002F\u002Fgithub.com\u002FPanQiWei\u002FAutoGPTQ)\n - [BitsAndBytes, accessible large language models via k-bit quantization](https:\u002F\u002Fgithub.com\u002FTimDettmers\u002Fbitsandbytes)\n - [GPTQ-for-LLaMa, 4 bits quantization of LLaMA using GPTQ](https:\u002F\u002Fgithub.com\u002Fqwopqwop200\u002FGPTQ-for-LLaMa)\n - [Optimum, acceleration of 🤗 Transformers and 🤗 Diffusers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Foptimum)\n\n## \u003Ca name='AgenticAI'>\u003C\u002Fa>Agentic AI\n- Multi-Agent Systems\n - [LangGraph, library for building stateful, multi-actor applications with LLMs](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph)\n - [AutoGen, library that enables the creation of applications using multiple agents that can converse with each other](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen)\n - [CrewAI, framework for orchestrating role-playing, autonomous AI agents](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002FcrewAI)\n - [MetaGPT, multi-agent framework for software company simulation](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT)\n - [AgentScope, user-friendly multi-agent platform](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fagentscope)\n - [Swarm, educational framework for building and deploying multi-agent systems](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm)\n- Autonomous Agents\n - [AutoGPT, autonomous GPT-4 experiment to make GPT-4 fully autonomous](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT)\n - [BabyAGI, example of an AI-powered task management system](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi)\n - [LangChain Agents, building agents with LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002Ftree\u002Fmaster\u002Flibs\u002Flangchain\u002Flangchain\u002Fagents)\n - [ReAct: Reasoning and Acting with Language Models](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Freact)\n - [Voyager, open-ended embodied agent with large language models](https:\u002F\u002Fgithub.com\u002FMineDojo\u002FVoyager)\n- Agent Orchestration and Frameworks \n - [Semantic Kernel, lightweight SDK for integrating AI services](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-kernel)\n - [OpenAI Function Calling, tools for function calling with OpenAI models](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-python)\n - [LlamaIndex Agents, data agents with LlamaIndex](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index\u002Ftree\u002Fmain\u002Fllama-index-core\u002Fllama_index\u002Fcore\u002Fagent)\n - [Haystack Agents, building search and QA agents](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack)\n - [DSPy, framework for algorithmically optimizing LM prompts and weights](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy)\n- Planning and Reasoning\n - [Tree of Thoughts, deliberate problem solving with large language models](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Ftree-of-thought-llm)\n - [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https:\u002F\u002Fgithub.com\u002Fbillxbf\u002FReWOO)\n - [Plan-and-Solve Prompting](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FPlan-and-Solve-Prompting)\n- Memory and Learning\n - [MemGPT, creating LLM agents with long-term memory](https:\u002F\u002Fgithub.com\u002Fcpacker\u002FMemGPT)\n - [Zep, fast, scalable building blocks for production LLM apps](https:\u002F\u002Fgithub.com\u002Fgetzep\u002Fzep)\n\n## \u003Ca name='GuardrailsandAISafety'>\u003C\u002Fa>Guardrails and AI Safety\n- Content Filtering and Moderation\n - [Guardrails AI, framework for building reliable AI applications](https:\u002F\u002Fgithub.com\u002Fguardrails-ai\u002Fguardrails)\n - [NeMo Guardrails, toolkit for building trustworthy, safe and secure LLM applications](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Guardrails)\n - [OpenAI Moderation API Tools](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fmoderation-api-release)\n - [Detoxify, toxic comment classification using transformer models](https:\u002F\u002Fgithub.com\u002Funitaryai\u002Fdetoxify)\n - [Perspective API PyTorch Implementation, toxicity detection](https:\u002F\u002Fgithub.com\u002Fconversationai\u002Fperspectiveapi)\n- Prompt Injection Defense\n - [Prompt Injection Detector, detecting prompt injection attacks](https:\u002F\u002Fgithub.com\u002Fprotectai\u002Frebuff)\n - [LLM Guard, security toolkit for LLM interactions](https:\u002F\u002Fgithub.com\u002Fprotectai\u002Fllm-guard)\n - [Garak, LLM vulnerability scanner](https:\u002F\u002Fgithub.com\u002Fleondz\u002Fgarak)\n- Bias Detection and Mitigation\n - [FairLearn, toolkit for assessing and improving fairness](https:\u002F\u002Fgithub.com\u002Ffairlearn\u002Ffairlearn)\n - [AIF360, comprehensive set of fairness metrics and bias mitigation algorithms](https:\u002F\u002Fgithub.com\u002FTrusted-AI\u002FAIF360)\n - [What-If Tool, tool for analyzing and understanding ML models](https:\u002F\u002Fgithub.com\u002FPAIR-code\u002Fwhat-if-tool)\n- Privacy and Security\n - [Opacus, library for training PyTorch models with differential privacy](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fopacus)\n - [PySyft, secure and private Deep Learning framework](https:\u002F\u002Fgithub.com\u002FOpenMined\u002FPySyft)\n - [CrypTen, framework for Privacy Preserving Machine Learning](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FCrypTen)\n - [Adversarial Robustness Toolbox, library for adversarial attacks and defenses](https:\u002F\u002Fgithub.com\u002FTrusted-AI\u002Fadversarial-robustness-toolbox)\n- Model Interpretability and Explainability\n - [LIME, explaining the predictions of machine learning classifiers](https:\u002F\u002Fgithub.com\u002Fmarcotcr\u002Flime)\n - [SHAP, unified approach to explain the output of machine learning models](https:\u002F\u002Fgithub.com\u002Fslundberg\u002Fshap)\n - [InterpretML, interpret and understand machine learning models](https:\u002F\u002Fgithub.com\u002Finterpretml\u002Finterpret)\n - [Alibi, algorithms for explaining machine learning models](https:\u002F\u002Fgithub.com\u002FSeldonIO\u002Falibi)\n- Safety Evaluation and Testing\n - [AI Safety Gym, environments and tools for AI safety research](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fsafety-gym)\n - [Anthropic's Constitutional AI implementations](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fconstitutional-ai)\n - [HarmBench, standardized evaluation framework for automated red teaming](https:\u002F\u002Fgithub.com\u002Fcenterforaisafety\u002FHarmBench)\n\n## \u003Ca name='TabularData'>\u003C\u002Fa>Tabular Data\n- [PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning](https:\u002F\u002Fgithub.com\u002Fpyg-team\u002Fpytorch-frame)\n- [Pytorch Tabular,standard framework for modelling Deep Learning Models for tabular data](https:\u002F\u002Fgithub.com\u002Fmanujosephv\u002Fpytorch_tabular)\n- [Tab Transformer](https:\u002F\u002Fgithub.com\u002Flucidrains\u002Ftab-transformer-pytorch)\n- [PyTorch-TabNet: Attentive Interpretable Tabular Learning](https:\u002F\u002Fgithub.com\u002Fdreamquark-ai\u002Ftabnet)\n- [carefree-learn: A minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch](https:\u002F\u002Fgithub.com\u002Fcarefree0910\u002Fcarefree-learn)\n\n## \u003Ca name='Visualization'>\u003C\u002Fa>Visualization\n- [Loss Visualization](https:\u002F\u002Fgithub.com\u002Ftomgoldstein\u002Floss-landscape)\n- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https:\u002F\u002Fgithub.com\u002Fjacobgil\u002Fpytorch-grad-cam)\n- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-visualizations)\n- [SmoothGrad: removing noise by adding noise](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-visualizations)\n- [DeepDream: dream-like hallucinogenic visuals](https:\u002F\u002Fgithub.com\u002FProGamerGov\u002Fneural-dream)\n- [FlashTorch: Visualization toolkit for neural networks in PyTorch](https:\u002F\u002Fgithub.com\u002FMisaOgura\u002Fflashtorch)\n- [Lucent: Lucid adapted for PyTorch](https:\u002F\u002Fgithub.com\u002Fgreentfrapp\u002Flucent)\n- [DreamCreator: Training GoogleNet models for DeepDream with custom datasets made simple](https:\u002F\u002Fgithub.com\u002FProGamerGov\u002Fdream-creator)\n- [CNN Feature Map Visualisation](https:\u002F\u002Fgithub.com\u002Flewis-morris\u002Fmapextrackt)\n\n## \u003Ca name='Explainability'>\u003C\u002Fa>Explainability\n- [Neural-Backed Decision Trees](https:\u002F\u002Fgithub.com\u002Falvinwan\u002Fneural-backed-decision-trees)\n- [Efficient Covariance Estimation from Temporal Data](https:\u002F\u002Fgithub.com\u002Fhrayrhar\u002FT-CorEx)\n- [Hierarchical interpretations for neural network predictions](https:\u002F\u002Fgithub.com\u002Fcsinva\u002Fhierarchical-dnn-interpretations)\n- [Shap, a unified approach to explain the output of any machine learning model](https:\u002F\u002Fgithub.com\u002Fslundberg\u002Fshap)\n- [VIsualizing PyTorch saved .pth deep learning models with netron](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fnetron)\n- [Distilling a Neural Network Into a Soft Decision Tree](https:\u002F\u002Fgithub.com\u002Fkimhc6028\u002Fsoft-decision-tree)\n- [Captum, A unified model interpretability library for PyTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fcaptum)\n\n## \u003Ca name='ObjectDetection'>\u003C\u002Fa>Object Detection\n- [MMDetection Object Detection Toolbox](https:\u002F\u002Fgithub.com\u002Fopen-mmlab\u002Fmmdetection)\n- [Mask R-CNN Benchmark: Faster R-CNN and Mask R-CNN in PyTorch 1.0](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fmaskrcnn-benchmark)\n- [YOLO-World](https:\u002F\u002Fgithub.com\u002FAILab-CVC\u002FYOLO-World)\n- [YOLOS](https:\u002F\u002Fgithub.com\u002Fhustvl\u002FYOLOS)\n- [YOLOF](https:\u002F\u002Fgithub.com\u002Fmegvii-model\u002FYOLOF)\n- [YOLOX](https:\u002F\u002Fgithub.com\u002FMegvii-BaseDetection\u002FYOLOX)\n- [YOLOv12: Attention-Centric Real-Time Object Detectors](https:\u002F\u002Fgithub.com\u002Fsunsmarterjie\u002Fyolov12)\n- [YOLOv11](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics)\n- [YOLOv10](https:\u002F\u002Fgithub.com\u002FTHU-MIG\u002Fyolov10)\n- [YOLOv9](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov9)\n- [YOLOv8](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics)\n- [Yolov7](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7)\n- [YOLOv6](https:\u002F\u002Fgithub.com\u002Fmeituan\u002FYOLOv6)\n- [Yolov5](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5)\n- [Yolov4](https:\u002F\u002Fgithub.com\u002FAlexeyAB\u002Fdarknet)\n- [YOLOv3](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3)\n- [YOLOv2: Real-Time Object Detection](https:\u002F\u002Fgithub.com\u002Flongcw\u002Fyolo2-pytorch)\n- [SSD: Single Shot MultiBox Detector](https:\u002F\u002Fgithub.com\u002Famdegroot\u002Fssd.pytorch)\n- [Detectron models for Object Detection](https:\u002F\u002Fgithub.com\u002Fignacio-rocco\u002Fdetectorch)\n- [Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks](https:\u002F\u002Fgithub.com\u002Fpotterhsu\u002FSVHNClassifier-PyTorch)\n- [Whale Detector](https:\u002F\u002Fgithub.com\u002FTarinZ\u002Fwhale-detector)\n- [Catalyst.Detection](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fdetection)\n\n## \u003Ca name='Long-TailedOut-of-DistributionRecognition'>\u003C\u002Fa>Long-Tailed \u002F Out-of-Distribution Recognition\n- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https:\u002F\u002Fgithub.com\u002Fkohpangwei\u002Fgroup_DRO)\n- [Invariant Risk Minimization](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FInvariantRiskMinimization)\n- [Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples](https:\u002F\u002Fgithub.com\u002Falinlab\u002FConfident_classifier)\n- [Deep Anomaly Detection with Outlier Exposure](https:\u002F\u002Fgithub.com\u002Fhendrycks\u002Foutlier-exposure)\n- [Large-Scale Long-Tailed Recognition in an Open World](https:\u002F\u002Fgithub.com\u002Fzhmiao\u002FOpenLongTailRecognition-OLTR)\n- [Principled Detection of Out-of-Distribution Examples in Neural Networks](https:\u002F\u002Fgithub.com\u002FShiyuLiang\u002Fodin-pytorch)\n- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https:\u002F\u002Fgithub.com\u002Fuoguelph-mlrg\u002Fconfidence_estimation)\n- [PyTorch Imbalanced Class Sampler](https:\u002F\u002Fgithub.com\u002Fufoym\u002Fimbalanced-dataset-sampler)\n\n## \u003Ca name='ActivationFunctions'>\u003C\u002Fa>Activation Functions\n- [Rational Activations - Learnable Rational Activation Functions](https:\u002F\u002Fgithub.com\u002Fml-research\u002Frational_activations)\n- [FreeGrad, PyTorch library for custom backward passes, straight-through estimators and gradient transforms.](https:\u002F\u002Fgithub.com\u002Ftbox98\u002FFreeGrad)\n\n## \u003Ca name='Energy-BasedLearning'>\u003C\u002Fa>Energy-Based Learning\n- [EBGAN, Energy-Based GANs](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FPyTorch-GAN\u002Fblob\u002Fmaster\u002Fimplementations\u002Febgan\u002Febgan.py)\n- [Maximum Entropy Generators for Energy-based Models](https:\u002F\u002Fgithub.com\u002Fritheshkumar95\u002Fenergy_based_generative_models)\n\n\n## \u003Ca name='MissingData'>\u003C\u002Fa>Missing Data\n - [BRITS: Bidirectional Recurrent Imputation for Time Series](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7911-brits-bidirectional-recurrent-imputation-for-time-series)\n\n## \u003Ca name='ArchitectureSearch'>\u003C\u002Fa>Architecture Search\n- [EfficientNetV2](https:\u002F\u002Fgithub.com\u002Flukemelas\u002FEfficientNet-PyTorch)\n- [DenseNAS](https:\u002F\u002Fgithub.com\u002FJaminFong\u002FDenseNAS)\n- [DARTS: Differentiable Architecture Search](https:\u002F\u002Fgithub.com\u002Fquark0\u002Fdarts)\n- [Efficient Neural Architecture Search (ENAS)](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002FENAS-pytorch)\n- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https:\u002F\u002Fgithub.com\u002Fzsef123\u002FEfficientNets-PyTorch)\n\n## \u003Ca name='ContinualLearning'>\u003C\u002Fa>Continual Learning\n- [Renate, Automatic Retraining of Neural Networks](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Frenate)\n\n## \u003Ca name='Optimization'>\u003C\u002Fa>Optimization\n- [AccSGD, AdaBound, AdaMod, DiffGrad, Lamb, NovoGrad, RAdam, SGDW, Yogi and more](https:\u002F\u002Fgithub.com\u002Fjettify\u002Fpytorch-optimizer)\n- [Lookahead Optimizer: k steps forward, 1 step back](https:\u002F\u002Fgithub.com\u002Falphadl\u002Flookahead.pytorch)\n- [RAdam, On the Variance of the Adaptive Learning Rate and Beyond](https:\u002F\u002Fgithub.com\u002FLiyuanLucasLiu\u002FRAdam)\n- [Over9000, Comparison of RAdam, Lookahead, Novograd, and combinations](https:\u002F\u002Fgithub.com\u002Fmgrankin\u002Fover9000)\n- [AdaBound, Train As Fast as Adam As Good as SGD](https:\u002F\u002Fgithub.com\u002FLuolc\u002FAdaBound)\n- [Riemannian Adaptive Optimization Methods](https:\u002F\u002Fgithub.com\u002Fferrine\u002Fgeoopt)\n- [L-BFGS](https:\u002F\u002Fgithub.com\u002Fhjmshi\u002FPyTorch-LBFGS)\n- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https:\u002F\u002Fgithub.com\u002Flocuslab\u002Foptnet)\n- [Learning to learn by gradient descent by gradient descent](https:\u002F\u002Fgithub.com\u002Fikostrikov\u002Fpytorch-meta-optimizer)\n- [Surrogate Gradient Learning in Spiking Neural Networks](https:\u002F\u002Fgithub.com\u002Ffzenke\u002Fspytorch)\n- [TorchOpt: An Efficient Library for Differentiable Optimization](https:\u002F\u002Fgithub.com\u002Fmetaopt\u002Ftorchopt)\n- [ph-training: Automatic Training with Persistent Homology](https:\u002F\u002Fgithub.com\u002Fneed-singularity\u002Fph-training) - Uses topological data analysis (H0 persistence) to predict difficulty, find optimal LR, and detect overfitting in real-time (r=0.998).\n## \u003Ca name='Quantization'>\u003C\u002Fa>Quantization\n- [Additive Power-of-Two Quantization: An Efficient Non-uniform Discretization For Neural Networks](https:\u002F\u002Fgithub.com\u002Fyhhhli\u002FAPoT_Quantization)\n\n## \u003Ca name='QuantumMachineLearning'>\u003C\u002Fa>Quantum Machine Learning\n- [Tor10, generic tensor-network library for quantum simulation in PyTorch](https:\u002F\u002Fgithub.com\u002Fkaihsin\u002FTor10)\n- [PennyLane, cross-platform Python library for quantum machine learning with PyTorch interface](https:\u002F\u002Fgithub.com\u002FXanaduAI\u002Fpennylane)\n- [QQA4CO, GPU-parallel Quasi-Quantum Annealing for combinatorial optimisation in PyTorch (ICLR 2025)](https:\u002F\u002Fgithub.com\u002FYuma-Ichikawa\u002FQQA4CO)\n\n## \u003Ca name='NeuralNetworkCompression'>\u003C\u002Fa>Neural Network Compression\n- [Bayesian Compression for Deep Learning](https:\u002F\u002Fgithub.com\u002FKarenUllrich\u002FTutorial_BayesianCompressionForDL)\n- [Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research](https:\u002F\u002Fgithub.com\u002FNervanaSystems\u002Fdistiller)\n- [Learning Sparse Neural Networks through L0 regularization](https:\u002F\u002Fgithub.com\u002FAMLab-Amsterdam\u002FL0_regularization)\n- [Energy-constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking](https:\u002F\u002Fgithub.com\u002Fhyang1990\u002Fmodel_based_energy_constrained_compression)\n- [EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis](https:\u002F\u002Fgithub.com\u002Falecwangcq\u002FEigenDamage-Pytorch)\n- [Pruning Convolutional Neural Networks for Resource Efficient Inference](https:\u002F\u002Fgithub.com\u002Fjacobgil\u002Fpytorch-pruning)\n- [Pruning neural networks: is it time to nip it in the bud? (showing reduced networks work better)](https:\u002F\u002Fgithub.com\u002FBayesWatch\u002Fpytorch-prunes)\n\n## \u003Ca name='FacialActionandPoseRecognition'>\u003C\u002Fa>Facial, Action and Pose Recognition\n- [Facenet: Pretrained Pytorch face detection and recognition models](https:\u002F\u002Fgithub.com\u002Ftimesler\u002Ffacenet-pytorch)\n- [DGC-Net: Dense Geometric Correspondence Network](https:\u002F\u002Fgithub.com\u002FAaltoVision\u002FDGC-Net)\n- [High performance facial recognition library on PyTorch](https:\u002F\u002Fgithub.com\u002FZhaoJ9014\u002Fface.evoLVe.PyTorch)\n- [FaceBoxes, a CPU real-time face detector with high accuracy](https:\u002F\u002Fgithub.com\u002Fzisianw\u002FFaceBoxes.PyTorch)\n- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https:\u002F\u002Fgithub.com\u002F1adrianb\u002Fface-alignment)\n- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https:\u002F\u002Fgithub.com\u002Fkenshohara\u002F3D-ResNets-PyTorch)\n- [PyTorch Realtime Multi-Person Pose Estimation](https:\u002F\u002Fgithub.com\u002FDavexPro\u002Fpytorch-pose-estimation)\n- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https:\u002F\u002Fgithub.com\u002Fclcarwin\u002Fsphereface_pytorch)\n- [GANimation: Anatomically-aware Facial Animation from a Single Image](https:\u002F\u002Fgithub.com\u002Falbertpumarola\u002FGANimation)\n- [Shufflenet V2 by Face++ with better results than paper](https:\u002F\u002Fgithub.com\u002Fericsun99\u002FShufflenet-v2-Pytorch)\n- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https:\u002F\u002Fgithub.com\u002Fxingyizhou\u002Fpytorch-pose-hg-3d)\n- [Unsupervised Learning of Depth and Ego-Motion from Video](https:\u002F\u002Fgithub.com\u002FClementPinard\u002FSfmLearner-Pytorch)\n- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fflownet2-pytorch)\n- [FlowNet: Learning Optical Flow with Convolutional Networks](https:\u002F\u002Fgithub.com\u002FClementPinard\u002FFlowNetPytorch)\n- [Optical Flow Estimation using a Spatial Pyramid Network](https:\u002F\u002Fgithub.com\u002Fsniklaus\u002Fpytorch-spynet)\n- [OpenFace in PyTorch](https:\u002F\u002Fgithub.com\u002Fthnkim\u002FOpenFacePytorch)\n- [Deep Face Recognition in PyTorch](https:\u002F\u002Fgithub.com\u002Fgrib0ed0v\u002Fface_recognition.pytorch)\n\n## \u003Ca name='Superresolution'>\u003C\u002Fa>Super resolution\n- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https:\u002F\u002Fgithub.com\u002Fthstkdgus35\u002FEDSR-PyTorch)\n- [Superresolution using an efficient sub-pixel convolutional neural network](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fexamples\u002Ftree\u002Fmaster\u002Fsuper_resolution)\n- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https:\u002F\u002Fgithub.com\u002Fbengxy\u002FFastNeuralStyle)\n\n## \u003Ca name='SynthetesizingViews'>\u003C\u002Fa>Synthetesizing Views\n- [NeRF, Neural Radian Fields, Synthesizing Novels Views of Complex Scenes](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002Fnerf-pytorch)\n\n## \u003Ca name='Voice'>\u003C\u002Fa>Voice\n- [Google AI VoiceFilter: Targeted Voice Separatation by Speaker-Conditioned Spectrogram Masking](https:\u002F\u002Fgithub.com\u002Fmindslab-ai\u002Fvoicefilter)\n\n## \u003Ca name='Medical'>\u003C\u002Fa>Medical\n- [Medical Zoo, 3D multi-modal medical image segmentation library in PyTorch]( https:\u002F\u002Fgithub.com\u002Fblack0017\u002FMedicalZooPytorch)\n- [U-Net for FLAIR Abnormality Segmentation in Brain MRI](https:\u002F\u002Fgithub.com\u002Fmateuszbuda\u002Fbrain-segmentation-pytorch)\n- [Genomic Classification via ULMFiT](https:\u002F\u002Fgithub.com\u002Fkheyer\u002FGenomic-ULMFiT)\n- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https:\u002F\u002Fgithub.com\u002Fnyukat\u002Fbreast_cancer_classifier)\n- [Delira, lightweight framework for medical imaging prototyping](https:\u002F\u002Fgithub.com\u002Fjustusschock\u002Fdelira)\n- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https:\u002F\u002Fgithub.com\u002Fmattmacy\u002Fvnet.pytorch)\n- [Medical Torch, medical imaging framework for PyTorch](https:\u002F\u002Fgithub.com\u002Fperone\u002Fmedicaltorch)\n- [TorchXRayVision - A library for chest X-ray datasets and models. Including pre-trainined models.](https:\u002F\u002Fgithub.com\u002Fmlmed\u002Ftorchxrayvision)\n\n## \u003Ca name='DSegmentationClassificationandRegression'>\u003C\u002Fa>3D Segmentation, Classification and Regression\n- [Kaolin, Library for Accelerating 3D Deep Learning Research](https:\u002F\u002Fgithub.com\u002FNVIDIAGameWorks\u002Fkaolin)\n- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https:\u002F\u002Fgithub.com\u002Ffxia22\u002Fpointnet.pytorch)\n- [3D segmentation with MONAI and Catalyst](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F15wJus5WZPYxTYE51yBhIBNhk9Tj4k3BT?usp=sharing)\n\n## \u003Ca name='VideoRecognition'>\u003C\u002Fa>Video Recognition\n- [Dancing to Music](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FDancing2Music)\n- [Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations](https:\u002F\u002Fgithub.com\u002Fnv-tlabs\u002FSTEAL)\n- [Deep Video Analytics](https:\u002F\u002Fgithub.com\u002FAKSHAYUBHAT\u002FDeepVideoAnalytics)\n- [PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs](https:\u002F\u002Fgithub.com\u002Fthuml\u002Fpredrnn-pytorch)\n\n## \u003Ca name='RecurrentNeuralNetworksRNNs'>\u003C\u002Fa>Recurrent Neural Networks (RNNs)\n- [SRU: training RNNs as fast as CNNs](https:\u002F\u002Fgithub.com\u002Fasappresearch\u002Fsru)\n- [Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks](https:\u002F\u002Fgithub.com\u002Fyikangshen\u002FOrdered-Neurons)\n- [Averaged Stochastic Gradient Descent with Weight Dropped LSTM](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002Fawd-lstm-lm)\n- [Training RNNs as Fast as CNNs](https:\u002F\u002Fgithub.com\u002Ftaolei87\u002Fsru)\n- [Quasi-Recurrent Neural Network (QRNN)](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002Fpytorch-qrnn)\n- [ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation](https:\u002F\u002Fgithub.com\u002FWizaron\u002Freseg-pytorch)\n- [A Recurrent Latent Variable Model for Sequential Data (VRNN)](https:\u002F\u002Fgithub.com\u002Femited\u002FVariationalRecurrentNeuralNetwork)\n- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https:\u002F\u002Fgithub.com\u002Fdasguptar\u002Ftreelstm.pytorch)\n- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https:\u002F\u002Fgithub.com\u002FDSKSD\u002FRNN-for-Joint-NLU)\n- [Attentive Recurrent Comparators](https:\u002F\u002Fgithub.com\u002Fsanyam5\u002Farc-pytorch)\n- [Collection of Sequence to Sequence Models with PyTorch](https:\u002F\u002Fgithub.com\u002FMaximumEntropy\u002FSeq2Seq-PyTorch)\n\t1. Vanilla Sequence to Sequence models\n\t2. Attention based Sequence to Sequence models\n\t3. Faster attention mechanisms using dot products between the final encoder and decoder hidden states\n\n## \u003Ca name='ConvolutionalNeuralNetworksCNNs'>\u003C\u002Fa>Convolutional Neural Networks (CNNs)\n- [LegoNet: Efficient Convolutional Neural Networks with Lego Filters](https:\u002F\u002Fgithub.com\u002Fhuawei-noah\u002FLegoNet)\n- [MeshCNN, a convolutional neural network designed specifically for triangular meshes](https:\u002F\u002Fgithub.com\u002Franahanocka\u002FMeshCNN)\n- [Octave Convolution](https:\u002F\u002Fgithub.com\u002Fd-li14\u002Foctconv.pytorch)\n- [PyTorch Image Models, ResNet\u002FResNeXT, DPN, MobileNet-V3\u002FV2\u002FV1, MNASNet, Single-Path NAS, FBNet](https:\u002F\u002Fgithub.com\u002Frwightman\u002Fpytorch-image-models)\n- [Deep Neural Networks with Box Convolutions](https:\u002F\u002Fgithub.com\u002Fshrubb\u002Fbox-convolutions)\n- [Invertible Residual Networks](https:\u002F\u002Fgithub.com\u002Fjarrelscy\u002FiResnet)\n- [Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks ](https:\u002F\u002Fgithub.com\u002Fxternalz\u002FSDPoint)\n- [Faster Faster R-CNN Implementation](https:\u002F\u002Fgithub.com\u002Fjwyang\u002Ffaster-rcnn.pytorch)\n\t- [Faster R-CNN Another Implementation](https:\u002F\u002Fgithub.com\u002Flongcw\u002Ffaster_rcnn_pytorch)\n- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https:\u002F\u002Fgithub.com\u002Fszagoruyko\u002Fattention-transfer)\n- [Wide ResNet model in PyTorch](https:\u002F\u002Fgithub.com\u002Fszagoruyko\u002Ffunctional-zoo)\n\t-[DiracNets: Training Very Deep Neural Networks Without Skip-Connections](https:\u002F\u002Fgithub.com\u002Fszagoruyko\u002Fdiracnets)\n- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https:\u002F\u002Fgithub.com\u002Fbgshih\u002Fcrnn)\n- [Efficient Densenet](https:\u002F\u002Fgithub.com\u002Fgpleiss\u002Fefficient_densenet_pytorch)\n- [Video Frame Interpolation via Adaptive Separable Convolution](https:\u002F\u002Fgithub.com\u002Fsniklaus\u002Fpytorch-sepconv)\n- [Learning local feature descriptors with triplets and shallow convolutional neural networks](https:\u002F\u002Fgithub.com\u002Fedgarriba\u002Fexamples\u002Ftree\u002Fmaster\u002Ftriplet)\n- [Densely Connected Convolutional Networks](https:\u002F\u002Fgithub.com\u002Fbamos\u002Fdensenet.pytorch)\n- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https:\u002F\u002Fgithub.com\u002Fjcjohnson\u002Fpytorch-vgg)\n- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \\\u003C0.5MB model size](https:\u002F\u002Fgithub.com\u002Fgsp-27\u002Fpytorch_Squeezenet)\n- [Deep Residual Learning for Image Recognition](https:\u002F\u002Fgithub.com\u002Fszagoruyko\u002Ffunctional-zoo)\n- [Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch](https:\u002F\u002Fgithub.com\u002Fxternalz\u002FWideResNet-pytorch)\n- [Deformable Convolutional Network](https:\u002F\u002Fgithub.com\u002Foeway\u002Fpytorch-deform-conv)\n- [Convolutional Neural Fabrics](https:\u002F\u002Fgithub.com\u002Fvabh\u002Fconvolutional-neural-fabrics)\n- [Deformable Convolutional Networks in PyTorch](https:\u002F\u002Fgithub.com\u002F1zb\u002Fdeformable-convolution-pytorch)\n- [Dilated ResNet combination with Dilated Convolutions](https:\u002F\u002Fgithub.com\u002Ffyu\u002Fdrn)\n- [Striving for Simplicity: The All Convolutional Net](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-visualizations)\n- [Convolutional LSTM Network](https:\u002F\u002Fgithub.com\u002Fautoman000\u002FConvolution_LSTM_pytorch)\n- [Big collection of pretrained classification models](https:\u002F\u002Fgithub.com\u002Fosmr\u002Fimgclsmob)\n- [PyTorch Image Classification with Kaggle Dogs vs Cats Dataset](https:\u002F\u002Fgithub.com\u002Frdcolema\u002Fpytorch-image-classification)\n- [CIFAR-10 on Pytorch with VGG, ResNet and DenseNet](https:\u002F\u002Fgithub.com\u002Fkuangliu\u002Fpytorch-cifar)\n- [Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)](https:\u002F\u002Fgithub.com\u002Faaron-xichen\u002Fpytorch-playground)\n- [NVIDIA\u002Funsupervised-video-interpolation](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Funsupervised-video-interpolation)\n\n## \u003Ca name='Segmentation'>\u003C\u002Fa>Segmentation\n- [Detectron2 by FAIR](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fdetectron2)\n- [Pixel-wise Segmentation on VOC2012 Dataset using PyTorch](https:\u002F\u002Fgithub.com\u002Fbodokaiser\u002Fpiwise)\n- [Pywick - High-level batteries-included neural network training library for Pytorch](https:\u002F\u002Fgithub.com\u002Fachaiah\u002Fpywick)\n- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fsemantic-segmentation)\n- [Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation](https:\u002F\u002Fgithub.com\u002FJianqiangWan\u002FSuper-BPD)\n- [Catalyst.Segmentation](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fsegmentation)\n- [Segmentation models with pretrained backbones](https:\u002F\u002Fgithub.com\u002Fqubvel\u002Fsegmentation_models.pytorch)\n\n## \u003Ca name='GeometricDeepLearning:GraphIrregularStructures'>\u003C\u002Fa>Geometric Deep Learning: Graph & Irregular Structures\n- [PyTorch Geometric, Deep Learning Extension](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric)\n- [PyTorch Geometric Temporal: A Temporal Extension Library for PyTorch Geometric](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fpytorch_geometric_temporal)\n- [PyTorch Geometric Signed Directed: A Signed & Directed Extension Library for PyTorch Geometric](https:\u002F\u002Fgithub.com\u002FSherylHYX\u002Fpytorch_geometric_signed_directed)\n- [ChemicalX: A PyTorch Based Deep Learning Library for Drug Pair Scoring](https:\u002F\u002Fgithub.com\u002FAstraZeneca\u002Fchemicalx)\n- [Self-Attention Graph Pooling](https:\u002F\u002Fgithub.com\u002Finyeoplee77\u002FSAGPool)\n- [Position-aware Graph Neural Networks](https:\u002F\u002Fgithub.com\u002FJiaxuanYou\u002FP-GNN)\n- [Signed Graph Convolutional Neural Network](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSGCN)\n- [Graph U-Nets](https:\u002F\u002Fgithub.com\u002FHongyangGao\u002Fgunet)\n- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FClusterGCN)\n- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FMixHop-and-N-GCN)\n- [Semi-Supervised Graph Classification: A Hierarchical Graph Perspective](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSEAL-CI)\n- [PyTorch BigGraph by FAIR for Generating Embeddings From Large-scale Graph Data](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FPyTorch-BigGraph)\n- [Capsule Graph Neural Network](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FCapsGNN)\n- [Splitter: Learning Node Representations that Capture Multiple Social Contexts](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSplitter)\n- [A Higher-Order Graph Convolutional Layer](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FMixHop-and-N-GCN)\n- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FAPPNP)\n- [Lorentz Embeddings: Learn Continuous Hierarchies in Hyperbolic Space](https:\u002F\u002Fgithub.com\u002FtheSage21\u002Florentz-embeddings)\n- [Graph Wavelet Neural Network](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FGraphWaveletNeuralNetwork)\n- [Watch Your Step: Learning Node Embeddings via Graph Attention](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FAttentionWalk)\n- [Signed Graph Convolutional Network](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSGCN)\n- [Graph Classification Using Structural Attention](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FGAM)\n- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSimGNN)\n- [SINE: Scalable Incomplete Network Embedding](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FSINE)\n- [HypER: Hypernetwork Knowledge Graph Embeddings](https:\u002F\u002Fgithub.com\u002Fibalazevic\u002FHypER)\n- [TuckER: Tensor Factorization for Knowledge Graph Completion](https:\u002F\u002Fgithub.com\u002Fibalazevic\u002FTuckER)\n- [PyKEEN: A Python library for learning and evaluating knowledge graph embeddings](https:\u002F\u002Fgithub.com\u002Fpykeen\u002Fpykeen\u002F)\n- [Pathfinder Discovery Networks for Neural Message Passing](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FPDN)\n- [SSSNET: Semi-Supervised Signed Network Clustering](https:\u002F\u002Fgithub.com\u002FSherylHYX\u002FSSSNET_Signed_Clustering)\n- [MagNet: A Neural Network for Directed Graphs](https:\u002F\u002Fgithub.com\u002Fmatthew-hirn\u002Fmagnet)\n- [PyTorch Geopooling: Geospatial Pooling Modules for Neural Networks in PyTorch](https:\u002F\u002Fgithub.com\u002Fybubnov\u002Ftorch_geopooling)\n\n## \u003Ca name='Sorting'>\u003C\u002Fa>Sorting\n- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https:\u002F\u002Fgithub.com\u002Fermongroup\u002Fneuralsort)\n\n## \u003Ca name='OrdinaryDifferentialEquationsNetworks'>\u003C\u002Fa>Ordinary Differential Equations Networks\n- [Latent ODEs for Irregularly-Sampled Time Series](https:\u002F\u002Fgithub.com\u002FYuliaRubanova\u002Flatent_ode)\n- [GRU-ODE-Bayes: continuous modelling of sporadically-observed time series](https:\u002F\u002Fgithub.com\u002Fedebrouwer\u002Fgru_ode_bayes)\n\n## \u003Ca name='Multi-taskLearning'>\u003C\u002Fa>Multi-task Learning\n- [Hierarchical Multi-Task Learning Model](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fhmtl)\n- [Task-based End-to-end Model Learning](https:\u002F\u002Fgithub.com\u002Flocuslab\u002Fe2e-model-learning)\n- [torchMTL: A lightweight module for Multi-Task Learning in pytorch](https:\u002F\u002Fgithub.com\u002Fchrisby\u002FtorchMTL)\n\n## \u003Ca name='GANsVAEsandAEs'>\u003C\u002Fa>GANs, VAEs, and AEs\n- [BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis](https:\u002F\u002Fgithub.com\u002Fajbrock\u002FBigGAN-PyTorch)\n- [High Fidelity Performance Metrics for Generative Models in PyTorch](https:\u002F\u002Fgithub.com\u002Ftoshas\u002Ftorch-fidelity)\n- [Mimicry, PyTorch Library for Reproducibility of GAN Research](https:\u002F\u002Fgithub.com\u002Fkwotsin\u002Fmimicry)\n- [Clean Readable CycleGAN](https:\u002F\u002Fgithub.com\u002Faitorzip\u002FPyTorch-CycleGAN)\n- [StarGAN](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fstargan)\n- [Block Neural Autoregressive Flow](https:\u002F\u002Fgithub.com\u002Fnicola-decao\u002FBNAF)\n- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpix2pixHD)\n- [A Style-Based Generator Architecture for Generative Adversarial Networks](https:\u002F\u002Fgithub.com\u002Frosinality\u002Fstyle-based-gan-pytorch)\n- [GANDissect, PyTorch Tool for Visualizing Neurons in GANs](https:\u002F\u002Fgithub.com\u002FCSAILVision\u002Fgandissect)\n- [Learning deep representations by mutual information estimation and maximization](https:\u002F\u002Fgithub.com\u002FDuaneNielsen\u002FDeepInfomaxPytorch)\n- [Variational Laplace Autoencoders](https:\u002F\u002Fgithub.com\u002Fyookoon\u002FVLAE)\n- [VeGANS, library for easily training GANs](https:\u002F\u002Fgithub.com\u002Funit8co\u002Fvegans)\n- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https:\u002F\u002Fgithub.com\u002Fgithub-pengge\u002FPyTorch-progressive_growing_of_gans)\n- [Conditional GAN](https:\u002F\u002Fgithub.com\u002Fkmualim\u002FCGAN-Pytorch\u002F)\n- [Wasserstein GAN](https:\u002F\u002Fgithub.com\u002Fmartinarjovsky\u002FWassersteinGAN)\n- [Adversarial Generator-Encoder Network](https:\u002F\u002Fgithub.com\u002FDmitryUlyanov\u002FAGE)\n- [Image-to-Image Translation with Conditional Adversarial Networks](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002Fpytorch-CycleGAN-and-pix2pix)\n- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002Fpytorch-CycleGAN-and-pix2pix)\n- [On the Effects of Batch and Weight Normalization in Generative Adversarial Networks](https:\u002F\u002Fgithub.com\u002Fstormraiser\u002FGAN-weight-norm)\n- [Improved Training of Wasserstein GANs](https:\u002F\u002Fgithub.com\u002Fjalola\u002Fimproved-wgan-pytorch)\n- [Collection of Generative Models with PyTorch](https:\u002F\u002Fgithub.com\u002Fwiseodd\u002Fgenerative-models)\n\t- Generative Adversarial Nets (GAN)\n\t\t1. [Vanilla GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.2661)\n\t\t2. [Conditional GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1411.1784)\n\t\t3. [InfoGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.03657)\n\t\t4. [Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07875)\n\t\t5. [Mode Regularized GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.02136)\n\t- Variational Autoencoder (VAE)\n\t\t1. [Vanilla VAE](https:\u002F\u002Farxiv.org\u002Fabs\u002F1312.6114)\n\t\t2. [Conditional VAE](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.5298)\n\t\t3. [Denoising VAE](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06406)\n\t\t4. [Adversarial Autoencoder](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.05644)\n\t\t5. [Adversarial Variational Bayes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.04722)\n- [Improved Training of Wasserstein GANs](https:\u002F\u002Fgithub.com\u002Fcaogang\u002Fwgan-gp)\n- [CycleGAN and Semi-Supervised GAN](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fmnist-svhn-transfer)\n- [Improving Variational Auto-Encoders using Householder Flow and using convex combination linear Inverse Autoregressive Flow](https:\u002F\u002Fgithub.com\u002Fjmtomczak\u002Fvae_vpflows)\n- [PyTorch GAN Collection](https:\u002F\u002Fgithub.com\u002Fznxlwm\u002Fpytorch-generative-model-collections)\n- [Generative Adversarial Networks, focusing on anime face drawing](https:\u002F\u002Fgithub.com\u002Fjayleicn\u002FanimeGAN)\n- [Simple Generative Adversarial Networks](https:\u002F\u002Fgithub.com\u002Fmailmahee\u002Fpytorch-generative-adversarial-networks)\n- [Adversarial Auto-encoders](https:\u002F\u002Fgithub.com\u002Ffducau\u002FAAE_pytorch)\n- [torchgan: Framework for modelling Generative Adversarial Networks in Pytorch](https:\u002F\u002Fgithub.com\u002Ftorchgan\u002Ftorchgan)\n- [Evaluating Lossy Compression Rates of Deep Generative Models](https:\u002F\u002Fgithub.com\u002Fhuangsicong\u002Frate_distortion)\n- [Catalyst.GAN](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fgan)\n 1. [Vanilla GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.2661)\n 2. [Conditional GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1411.1784)\n 3. [Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07875)\n 4. [Improved Training of Wasserstein GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.00028)\n\n## \u003Ca name='UnsupervisedLearning'>\u003C\u002Fa>Unsupervised Learning\n- [Unsupervised Embedding Learning via Invariant and Spreading Instance Feature](https:\u002F\u002Fgithub.com\u002Fmangye16\u002FUnsupervised_Embedding_Learning)\n- [AND: Anchor Neighbourhood Discovery](https:\u002F\u002Fgithub.com\u002FRaymond-sci\u002FAND)\n\n## \u003Ca name='AdversarialAttacks'>\u003C\u002Fa>Adversarial Attacks\n- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-adversarial-attacks)\n- [Explaining and Harnessing Adversarial Examples](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-adversarial-attacks)\n- [AdverTorch - A Toolbox for Adversarial Robustness Research](https:\u002F\u002Fgithub.com\u002FBorealisAI\u002Fadvertorch)\n\n## \u003Ca name='StyleTransfer'>\u003C\u002Fa>Style Transfer\n- [Pystiche: Framework for Neural Style Transfer](https:\u002F\u002Fgithub.com\u002Fpystiche\u002Fpystiche)\n- [Detecting Adversarial Examples via Neural Fingerprinting](https:\u002F\u002Fgithub.com\u002FStephanZheng\u002Fneural-fingerprinting)\n- [A Neural Algorithm of Artistic Style](https:\u002F\u002Fgithub.com\u002Falexis-jacq\u002FPytorch-Tutorials)\n- [Multi-style Generative Network for Real-time Transfer](https:\u002F\u002Fgithub.com\u002Fzhanghang1989\u002FPyTorch-Style-Transfer)\n- [DeOldify, Coloring Old Images](https:\u002F\u002Fgithub.com\u002Fjantic\u002FDeOldify)\n- [Neural Style Transfer](https:\u002F\u002Fgithub.com\u002FProGamerGov\u002Fneural-style-pt)\n- [Fast Neural Style Transfer](https:\u002F\u002Fgithub.com\u002Fdarkstar112358\u002Ffast-neural-style)\n- [Draw like Bob Ross](https:\u002F\u002Fgithub.com\u002Fkendricktan\u002Fdrawlikebobross)\n\n## \u003Ca name='ImageCaptioning'>\u003C\u002Fa>Image Captioning\n- [CLIP (Contrastive Language-Image Pre-Training)](https:\u002F\u002Fgithub.com\u002Fopenai\u002FCLIP)\n- [Neuraltalk 2, Image Captioning Model, in PyTorch](https:\u002F\u002Fgithub.com\u002Fruotianluo\u002Fneuraltalk2.pytorch)\n- [Generate captions from an image with PyTorch](https:\u002F\u002Fgithub.com\u002Feladhoffer\u002FcaptionGen)\n- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https:\u002F\u002Fgithub.com\u002Fjcjohnson\u002Fdensecap)\n\n## \u003Ca name='Transformers'>\u003C\u002Fa>Transformers\n- [Attention is all you need](https:\u002F\u002Fgithub.com\u002Fjadore801120\u002Fattention-is-all-you-need-pytorch)\n- [Spatial Transformer Networks](https:\u002F\u002Fgithub.com\u002Ffxia22\u002Fstn.pytorch)\n\n## \u003Ca name='SimilarityNetworksandFunctions'>\u003C\u002Fa>Similarity Networks and Functions\n- [Conditional Similarity Networks](https:\u002F\u002Fgithub.com\u002Fandreasveit\u002Fconditional-similarity-networks)\n\n## \u003Ca name='Reasoning'>\u003C\u002Fa>Reasoning\n- [Inferring and Executing Programs for Visual Reasoning](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fclevr-iep)\n\n## \u003Ca name='GeneralNLP'>\u003C\u002Fa>General NLP\n- [nanoGPT, fastest repository for training\u002Ffinetuning medium-sized GPTs](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002FnanoGPT)\n- [minGPT, Re-implementation of GPT to be small, clean, interpretable and educational](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002FminGPT)\n- [Espresso, Module Neural Automatic Speech Recognition Toolkit](https:\u002F\u002Fgithub.com\u002Ffreewym\u002Fespresso)\n- [Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification](https:\u002F\u002Fgithub.com\u002FHX-idiot\u002FHybrid_Attention_XML)\n- [XLNet](https:\u002F\u002Fgithub.com\u002Fgraykode\u002Fxlnet-Pytorch)\n- [Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading](https:\u002F\u002Fgithub.com\u002Fqkaren\u002Fconverse_reading_cmr)\n- [Cross-lingual Language Model Pretraining](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FXLM)\n- [Libre Office Translate via PyTorch NMT](https:\u002F\u002Fgithub.com\u002Flernapparat\u002Flotranslate)\n- [BERT](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpytorch-pretrained-BERT)\n- [VSE++: Improved Visual-Semantic Embeddings](https:\u002F\u002Fgithub.com\u002Ffartashf\u002Fvsepp)\n- [A Structured Self-Attentive Sentence Embedding](https:\u002F\u002Fgithub.com\u002FExplorerFreda\u002FStructured-Self-Attentive-Sentence-Embedding)\n- [Neural Sequence labeling model](https:\u002F\u002Fgithub.com\u002Fjiesutd\u002FPyTorchSeqLabel)\n- [Skip-Thought Vectors](https:\u002F\u002Fgithub.com\u002Fsanyam5\u002Fskip-thoughts)\n- [Complete Suite for Training Seq2Seq Models in PyTorch](https:\u002F\u002Fgithub.com\u002Feladhoffer\u002Fseq2seq.pytorch)\n- [MUSE: Multilingual Unsupervised and Supervised Embeddings](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FMUSE)\n- [TorchMoji: PyTorch Implementation of DeepMoji to under Language used to Express Emotions](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002FtorchMoji)\n\n## \u003Ca name='QuestionandAnswering'>\u003C\u002Fa>Question and Answering\n- [Visual Question Answering in Pytorch](https:\u002F\u002Fgithub.com\u002FCadene\u002Fvqa.pytorch)\n- [Reading Wikipedia to Answer Open-Domain Questions](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FDrQA)\n- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fend-to-end-negotiator)\n- [Interpretable Counting for Visual Question Answering](https:\u002F\u002Fgithub.com\u002Fsanyam5\u002Firlc-vqa)\n- [Open Source Chatbot with PyTorch](https:\u002F\u002Fgithub.com\u002Fjinfagang\u002Fpytorch_chatbot)\n\n## \u003Ca name='SpeechGenerationandRecognition'>\u003C\u002Fa>Speech Generation and Recognition\n- [PyTorch-Kaldi Speech Recognition Toolkit](https:\u002F\u002Fgithub.com\u002Fmravanelli\u002Fpytorch-kaldi)\n- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwaveglow)\n- [OpenNMT](https:\u002F\u002Fgithub.com\u002FOpenNMT\u002FOpenNMT-py)\n- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https:\u002F\u002Fgithub.com\u002FSeanNaren\u002Fdeepspeech.pytorch)\n- [WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit](https:\u002F\u002Fgithub.com\u002Fmobvoi\u002Fwenet)\n\n## \u003Ca name='DocumentandTextClassification'>\u003C\u002Fa>Document and Text Classification\n- [Hierarchical Attention Network for Document Classification](https:\u002F\u002Fgithub.com\u002Fcedias\u002FHAN-pytorch)\n- [Hierarchical Attention Networks for Document Classification](https:\u002F\u002Fgithub.com\u002FEdGENetworks\u002Fattention-networks-for-classification)\n- [CNN Based Text Classification](https:\u002F\u002Fgithub.com\u002Fxiayandi\u002FPytorch_text_classification)\n\n## \u003Ca name='TextGeneration'>\u003C\u002Fa>Text Generation\n- [Pytorch Poetry Generation](https:\u002F\u002Fgithub.com\u002Fjhave\u002Fpytorch-poetry-generation)\n\n## \u003Ca name='TexttoImage'>\u003C\u002Fa>Text to Image\n- [Stable Diffusion](https:\u002F\u002Fgithub.com\u002FCompVis\u002Fstable-diffusion)\n- [Dall-E 2](https:\u002F\u002Fgithub.com\u002Flucidrains\u002FDALLE2-pytorch)\n- [Dall-E](https:\u002F\u002Fgithub.com\u002Flucidrains\u002FDALLE-pytorch)\n\n## \u003Ca name='Translation'>\u003C\u002Fa>Translation\n- [Open-source (MIT) Neural Machine Translation (NMT) System](https:\u002F\u002Fgithub.com\u002FOpenNMT\u002FOpenNMT-py)\n\n## \u003Ca name='SentimentAnalysis'>\u003C\u002Fa>Sentiment Analysis\n- [Recurrent Neural Networks for Sentiment Analysis (Aspect-Based) on SemEval 2014](https:\u002F\u002Fgithub.com\u002Fvanzytay\u002Fpytorch_sentiment_rnn)\n- [Seq2Seq Intent Parsing](https:\u002F\u002Fgithub.com\u002Fspro\u002Fpytorch-seq2seq-intent-parsing)\n- [Finetuning BERT for Sentiment Analysis](https:\u002F\u002Fgithub.com\u002Fbarissayil\u002FSentimentAnalysis)\n\n## \u003Ca name='DeepReinforcementLearning'>\u003C\u002Fa>Deep Reinforcement Learning\n- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https:\u002F\u002Fgithub.com\u002Fdenisyarats\u002Fdrq)\n- [Exploration by Random Network Distillation](https:\u002F\u002Fgithub.com\u002Fopenai\u002Frandom-network-distillation)\n- [EGG: Emergence of lanGuage in Games, quickly implement multi-agent games with discrete channel communication](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FEGG)\n- [Temporal Difference VAE](https:\u002F\u002Fopenreview.net\u002Fpdf?id=S1x4ghC9tQ)\n- [High-performance Atari A3C Agent in 180 Lines PyTorch](https:\u002F\u002Fgithub.com\u002Fgreydanus\u002Fbaby-a3c)\n- [Learning when to communicate at scale in multiagent cooperative and competitive tasks](https:\u002F\u002Fgithub.com\u002FIC3Net\u002FIC3Net)\n- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https:\u002F\u002Fgithub.com\u002Fshariqiqbal2810\u002FMAAC)\n- [PPO in PyTorch C++](https:\u002F\u002Fgithub.com\u002Fmhubii\u002Fppo_pytorch_cpp)\n- [Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback](https:\u002F\u002Fgithub.com\u002Fkhanhptnk\u002Fbandit-nmt)\n- [Asynchronous Methods for Deep Reinforcement Learning](https:\u002F\u002Fgithub.com\u002Fikostrikov\u002Fpytorch-a3c)\n- [Continuous Deep Q-Learning with Model-based Acceleration](https:\u002F\u002Fgithub.com\u002Fikostrikov\u002Fpytorch-naf)\n- [Asynchronous Methods for Deep Reinforcement Learning for Atari 2600](https:\u002F\u002Fgithub.com\u002Fdgriff777\u002Frl_a3c_pytorch)\n- [Trust Region Policy Optimization](https:\u002F\u002Fgithub.com\u002Fmjacar\u002Fpytorch-trpo)\n- [Neural Combinatorial Optimization with Reinforcement Learning](https:\u002F\u002Fgithub.com\u002Fpemami4911\u002Fneural-combinatorial-rl-pytorch)\n- [Noisy Networks for Exploration](https:\u002F\u002Fgithub.com\u002FKaixhin\u002FNoisyNet-A3C)\n- [Distributed Proximal Policy Optimization](https:\u002F\u002Fgithub.com\u002Falexis-jacq\u002FPytorch-DPPO)\n- [Reinforcement learning models in ViZDoom environment with PyTorch](https:\u002F\u002Fgithub.com\u002Fakolishchak\u002Fdoom-net-pytorch)\n- [Reinforcement learning models using Gym and Pytorch](https:\u002F\u002Fgithub.com\u002Fjingweiz\u002Fpytorch-rl)\n- [SLM-Lab: Modular Deep Reinforcement Learning framework in PyTorch](https:\u002F\u002Fgithub.com\u002Fkengz\u002FSLM-Lab)\n- [Catalyst.RL](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fcatalyst-rl)\n\n## \u003Ca name='DeepBayesianLearningandProbabilisticProgrammming'>\u003C\u002Fa>Deep Bayesian Learning and Probabilistic Programmming\n- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https:\u002F\u002Fgithub.com\u002FBlackHC\u002FBatchBALD)\n- [Subspace Inference for Bayesian Deep Learning](https:\u002F\u002Fgithub.com\u002Fwjmaddox\u002Fdrbayes)\n- [Bayesian Deep Learning with Variational Inference Package](https:\u002F\u002Fgithub.com\u002Fctallec\u002Fpyvarinf)\n- [Probabilistic Programming and Statistical Inference in PyTorch](https:\u002F\u002Fgithub.com\u002Fstepelu\u002Fptstat)\n- [Bayesian CNN with Variational Inferece in PyTorch](https:\u002F\u002Fgithub.com\u002Fkumar-shridhar\u002FPyTorch-BayesianCNN)\n\n## \u003Ca name='SpikingNeuralNetworks'>\u003C\u002Fa>Spiking Neural Networks\n- [Norse, Library for Deep Learning with Spiking Neural Networks](https:\u002F\u002Fgithub.com\u002Fnorse\u002Fnorse)\n\n## \u003Ca name='AnomalyDetection'>\u003C\u002Fa>Anomaly Detection\n- [Detection of Accounting Anomalies using Deep Autoencoder Neural Networks](https:\u002F\u002Fgithub.com\u002FGitiHubi\u002FdeepAI)\n\n## \u003Ca name='RegressionTypes'>\u003C\u002Fa>Regression Types\n- [Quantile Regression DQN](https:\u002F\u002Fgithub.com\u002Fars-ashuha\u002Fquantile-regression-dqn-pytorch)\n\n## \u003Ca name='TimeSeries'>\u003C\u002Fa>Time Series\n- [Dual Self-Attention Network for Multivariate Time Series Forecasting](https:\u002F\u002Fgithub.com\u002Fbighuang624\u002FDSANet)\n- [DILATE: DIstortion Loss with shApe and tImE](https:\u002F\u002Fgithub.com\u002Fvincent-leguen\u002FDILATE)\n- [Variational Recurrent Autoencoder for Timeseries Clustering](https:\u002F\u002Fgithub.com\u002Ftejaslodaya\u002Ftimeseries-clustering-vae)\n- [Spatio-Temporal Neural Networks for Space-Time Series Modeling and Relations Discovery](https:\u002F\u002Fgithub.com\u002Fedouardelasalles\u002Fstnn)\n- [Flow Forecast: A deep learning for time series forecasting framework built in PyTorch](https:\u002F\u002Fgithub.com\u002FAIStream-Peelout\u002Fflow-forecast)\n\n## \u003Ca name='SyntheticDatasets'>\u003C\u002Fa>Synthetic Datasets\n- [Meta-Sim: Learning to Generate Synthetic Datasets](https:\u002F\u002Fgithub.com\u002Fnv-tlabs\u002Fmeta-sim)\n\n## \u003Ca name='NeuralNetworkGeneralImprovements'>\u003C\u002Fa>Neural Network General Improvements\n- [PH Training, persistent homology-based training monitor that detects overfitting early using topological data analysis](https:\u002F\u002Fgithub.com\u002Fneed-singularity\u002Fph-training)\n- [The Artificial Dendrite Network Library for PyTorch](https:\u002F\u002Fgithub.com\u002FPerforatedAI\u002FPerforatedAI)\n- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https:\u002F\u002Fgithub.com\u002Fmapillary\u002Finplace_abn)\n- [Train longer, generalize better: closing the generalization gap in large batch training of neural networks](https:\u002F\u002Fgithub.com\u002Feladhoffer\u002FbigBatch)\n- [FreezeOut: Accelerate Training by Progressively Freezing Layers](https:\u002F\u002Fgithub.com\u002Fajbrock\u002FFreezeOut)\n- [Binary Stochastic Neurons](https:\u002F\u002Fgithub.com\u002FWizaron\u002Fbinary-stochastic-neurons)\n- [Compact Bilinear Pooling](https:\u002F\u002Fgithub.com\u002FDeepInsight-PCALab\u002FCompactBilinearPooling-Pytorch)\n- [Mixed Precision Training in PyTorch](https:\u002F\u002Fgithub.com\u002Fsuvojit-0x55aa\u002Fmixed-precision-pytorch)\n\n## \u003Ca name='DNNApplicationsinChemistryandPhysics'>\u003C\u002Fa>DNN Applications in Chemistry and Physics\n- [Wave Physics as an Analog Recurrent Neural Network](https:\u002F\u002Fgithub.com\u002Ffancompute\u002Fwavetorch)\n- [Neural Message Passing for Quantum Chemistry](https:\u002F\u002Fgithub.com\u002Fpriba\u002Fnmp_qc)\n- [Automatic chemical design using a data-driven continuous representation of molecules](https:\u002F\u002Fgithub.com\u002Fcxhernandez\u002Fmolencoder)\n- [Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge](https:\u002F\u002Fgithub.com\u002Femited\u002Fflow)\n- [Differentiable Molecular Simulation for Learning and Control](https:\u002F\u002Fgithub.com\u002Fwwang2\u002Ftorchmd)\n\n## \u003Ca name='NewThinkingonGeneralNeuralNetworkArchitecture'>\u003C\u002Fa>New Thinking on General Neural Network Architecture\n- [Complement Objective Training](https:\u002F\u002Fgithub.com\u002Fhenry8527\u002FCOT)\n- [Decoupled Neural Interfaces using Synthetic Gradients](https:\u002F\u002Fgithub.com\u002Fandrewliao11\u002Fdni.pytorch)\n\n## \u003Ca name='LinearAlgebra'>\u003C\u002Fa>Linear Algebra\n- [Eigenvectors from Eigenvalues](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Feigenvectors-from-eigenvalues)\n\n## \u003Ca name='APIAbstraction'>\u003C\u002Fa>API Abstraction\n- [Torch Layers, Shape inference for PyTorch, SOTA Layers](https:\u002F\u002Fgithub.com\u002Fszymonmaszke\u002Ftorchlayers)\n- [Hummingbird, run trained scikit-learn models on GPU with PyTorch](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhummingbird)\n\n## \u003Ca name='LowLevelUtilities'>\u003C\u002Fa>Low Level Utilities\n- [TorchSharp, .NET API with access to underlying library powering PyTorch](https:\u002F\u002Fgithub.com\u002Finteresaaat\u002FTorchSharp)\n\n## \u003Ca name='PyTorchUtilities'>\u003C\u002Fa>PyTorch Utilities\n- [Functorch: prototype of JAX-like composable Function transformers for PyTorch](https:\u002F\u002Fgithub.com\u002Fzou3519\u002Ffunctorch)\n- [Poutyne: Simplified Framework for Training Neural Networks](https:\u002F\u002Fgithub.com\u002FGRAAL-Research\u002Fpoutyne)\n- [PyTorch Metric Learning](https:\u002F\u002Fgithub.com\u002FKevinMusgrave\u002Fpytorch-metric-learning)\n- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https:\u002F\u002Fkornia.org\u002F)\n- [BackPACK to easily Extract Variance, Diagonal of Gauss-Newton, and KFAC](https:\u002F\u002Ff-dangel.github.io\u002Fbackpack\u002F)\n- [PyHessian for Computing Hessian Eigenvalues, trace of matrix, and ESD](https:\u002F\u002Fgithub.com\u002Famirgholami\u002FPyHessian)\n- [Hessian in PyTorch](https:\u002F\u002Fgithub.com\u002Fmariogeiger\u002Fhessian)\n- [Differentiable Convex Layers](https:\u002F\u002Fgithub.com\u002Fcvxgrp\u002Fcvxpylayers)\n- [Albumentations: Fast Image Augmentation Library](https:\u002F\u002Fgithub.com\u002Falbu\u002Falbumentations)\n- [Higher, obtain higher order gradients over losses spanning training loops](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhigher)\n- [Neural Pipeline, Training Pipeline for PyTorch](https:\u002F\u002Fgithub.com\u002Ftoodef\u002Fneural-pipeline)\n- [Layer-by-layer PyTorch Model Profiler for Checking Model Time Consumption](https:\u002F\u002Fgithub.com\u002Fawwong1\u002Ftorchprof)\n- [Sparse Distributions](https:\u002F\u002Fgithub.com\u002Fprobabll\u002Fsparse-distributions)\n- [Diffdist, Adds Support for Differentiable Communication allowing distributed model parallelism](https:\u002F\u002Fgithub.com\u002Fag14774\u002Fdiffdist)\n- [HessianFlow, Library for Hessian Based Algorithms](https:\u002F\u002Fgithub.com\u002Famirgholami\u002FHessianFlow)\n- [Texar, PyTorch Toolkit for Text Generation](https:\u002F\u002Fgithub.com\u002Fasyml\u002Ftexar-pytorch)\n- [PyTorch FLOPs counter](https:\u002F\u002Fgithub.com\u002FLyken17\u002Fpytorch-OpCounter)\n- [PyTorch Inference on C++ in Windows](https:\u002F\u002Fgithub.com\u002Fzccyman\u002Fpytorch-inference)\n- [EuclidesDB, Multi-Model Machine Learning Feature Database](https:\u002F\u002Fgithub.com\u002Fperone\u002Feuclidesdb)\n- [Data Augmentation and Sampling for Pytorch](https:\u002F\u002Fgithub.com\u002Fncullen93\u002Ftorchsample)\n- [PyText, deep learning based NLP modelling framework officially maintained by FAIR](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fpytext)\n- [Torchstat for Statistics on PyTorch Models](https:\u002F\u002Fgithub.com\u002FSwall0w\u002Ftorchstat)\n- [Load Audio files directly into PyTorch Tensors](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Faudio)\n- [Weight Initializations](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch\u002Fblob\u002Fmaster\u002Ftorch\u002Fnn\u002Finit.py)\n- [Spatial transformer implemented in PyTorch](https:\u002F\u002Fgithub.com\u002Ffxia22\u002Fstn.pytorch)\n- [PyTorch AWS AMI, run PyTorch with GPU support in less than 5 minutes](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fdlami)\n- [Use tensorboard with PyTorch](https:\u002F\u002Fgithub.com\u002Flanpa\u002Ftensorboard-pytorch)\n- [Simple Fit Module in PyTorch, similar to Keras](https:\u002F\u002Fgithub.com\u002Fhenryre\u002Fpytorch-fitmodule)\n- [torchbearer: A model fitting library for PyTorch](https:\u002F\u002Fgithub.com\u002Fecs-vlc\u002Ftorchbearer)\n- [PyTorch to Keras model converter](https:\u002F\u002Fgithub.com\u002Fnerox8664\u002Fpytorch2keras)\n- [Gluon to PyTorch model converter with code generation](https:\u002F\u002Fgithub.com\u002Fnerox8664\u002Fgluon2pytorch)\n- [Catalyst: High-level utils for PyTorch DL & RL research](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fcatalyst)\n- [PyTorch Lightning: Scalable and lightweight deep learning research framework](https:\u002F\u002Fgithub.com\u002FPyTorchLightning\u002Fpytorch-lightning)\n- [Determined: Scalable deep learning platform with PyTorch support](https:\u002F\u002Fgithub.com\u002Fdetermined-ai\u002Fdetermined)\n- [PyTorch-Ignite: High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fignite)\n- [torchvision: A package consisting of popular datasets, model architectures, and common image transformations for computer vision.](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fvision)\n- [Poutyne: A Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.](https:\u002F\u002Fgithub.com\u002FGRAAL-Research\u002Fpoutyne)\n- [torchensemble: Scikit-Learn like ensemble methods in PyTorch](https:\u002F\u002Fgithub.com\u002FAaronX121\u002FEnsemble-Pytorch)\n- [TorchFix - a linter for PyTorch-using code with autofix support](https:\u002F\u002Fgithub.com\u002Fpytorch-labs\u002Ftorchfix)\n- [pytorch360convert - Differentiable image conversions between 360° equirectangular images, cubemaps, and perspective projections](https:\u002F\u002Fgithub.com\u002FProGamerGov\u002Fpytorch360convert)\n- [torchcurves - differentiable parametric curve modules for PyTorch](https:\u002F\u002Fgithub.com\u002Falexshtf\u002Ftorchcurves)\n\n\n## \u003Ca name='PyTorchVideoTutorials'>\u003C\u002Fa>PyTorch Video Tutorials\n- [PyTorch Zero to All Lectures](http:\u002F\u002Fbit.ly\u002FPyTorchVideo)\n- [PyTorch For Deep Learning Full Course](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GIsg-ZUy0MY)\n- [PyTorch Lightning 101 with Alfredo Canziani and William Falcon](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)\n- [Practical Deep Learning with PyTorch](https:\u002F\u002Fwww.udemy.com\u002Fpractical-deep-learning-with-pytorch)\n\n\n## \u003Ca name='Community'>\u003C\u002Fa>Community\n- [PyTorch Discussion Forum](https:\u002F\u002Fdiscuss.pytorch.org\u002F)\n- [StackOverflow PyTorch Tags](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fpytorch)\n- [Catalyst.Slack](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Fcatalyst-team-core\u002Fshared_invite\u002Fzt-d9miirnn-z86oKDzFMKlMG4fgFdZafw)\n\n\n## \u003Ca name='TobeClassified'>\u003C\u002Fa>To be Classified\n- [Perturbative Neural Networks](https:\u002F\u002Fgithub.com\u002Fmichaelklachko\u002Fpnn.pytorch)\n- [Accurate Neural Network Potential](https:\u002F\u002Fgithub.com\u002Faiqm\u002Ftorchani)\n- [Scaling the Scattering Transform: Deep Hybrid Networks](https:\u002F\u002Fgithub.com\u002Fedouardoyallon\u002Fpyscatwave)\n- [CortexNet: a Generic Network Family for Robust Visual Temporal Representations](https:\u002F\u002Fgithub.com\u002Fe-lab\u002Fpytorch-CortexNet)\n- [Oriented Response Networks](https:\u002F\u002Fgithub.com\u002FZhouYanzhao\u002FORN)\n- [Associative Compression Networks](https:\u002F\u002Fgithub.com\u002Fjalexvig\u002Fassociative_compression_networks)\n- [Clarinet](https:\u002F\u002Fgithub.com\u002Fksw0306\u002FClariNet)\n- [Continuous Wavelet Transforms](https:\u002F\u002Fgithub.com\u002Ftomrunia\u002FPyTorchWavelets)\n- [mixup: Beyond Empirical Risk Minimization](https:\u002F\u002Fgithub.com\u002Fleehomyc\u002Fmixup_pytorch)\n- [Network In Network](https:\u002F\u002Fgithub.com\u002Fszagoruyko\u002Ffunctional-zoo)\n- [Highway Networks](https:\u002F\u002Fgithub.com\u002Fc0nn3r\u002Fpytorch_highway_networks)\n- [Hybrid computing using a neural network with dynamic external memory](https:\u002F\u002Fgithub.com\u002Fypxie\u002Fpytorch-NeuCom)\n- [Value Iteration Networks](https:\u002F\u002Fgithub.com\u002Fonlytailei\u002FPyTorch-value-iteration-networks)\n- [Differentiable Neural Computer](https:\u002F\u002Fgithub.com\u002Fjingweiz\u002Fpytorch-dnc)\n- [A Neural Representation of Sketch Drawings](https:\u002F\u002Fgithub.com\u002Falexis-jacq\u002FPytorch-Sketch-RNN)\n- [Understanding Deep Image Representations by Inverting Them](https:\u002F\u002Fgithub.com\u002Futkuozbulak\u002Fpytorch-cnn-visualizations)\n- [NIMA: Neural Image Assessment](https:\u002F\u002Fgithub.com\u002Ftruskovskiyk\u002Fnima.pytorch)\n- [NASNet-A-Mobile. Ported weights](https:\u002F\u002Fgithub.com\u002Fveronikayurchuk\u002Fpretrained-models.pytorch)\n- [Graphics code generating model using Processing](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fsketchnet)\n\n## \u003Ca name='LinkstoThisRepository'>\u003C\u002Fa>Links to This Repository\n- [Github Repository](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fthe-incredible-pytorch)\n- [Website](https:\u002F\u002Fwww.ritchieng.com\u002Fthe-incredible-pytorch\u002F)\n\n\n## \u003Ca name='Contributions'>\u003C\u002Fa>Contributions\nDo feel free to c","这个项目是一个精心整理的PyTorch相关资源列表，包括教程、论文、项目、社区等。它汇集了深度学习库PyTorch的各种应用场景和技术细节，涵盖了从基础入门到高级应用的广泛内容，如大型语言模型、图像识别、自然语言处理等。适合正在使用或计划使用PyTorch进行研究与开发的开发者、研究人员以及学生参考学习。通过该项目，用户可以快速找到所需的学习资料和实际案例，提高工作效率。","2026-06-11 03:24:14","top_topic"]