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Language Tutorials","Programming Language","Machine Learning","Computer Graphics","Game Engines","Mobile Development","Containers \u0026 Language Extentions \u0026 Linting","Desktop App Development","Machine Learning Tutorials","Data Processing","Cross-Platform","Deep Learning Framework","Reinforcement Learning","Causal Inference","Recommendation, Advertisement \u0026 Ranking","Other Machine Learning Applications","Linear Algebra / Statistics Toolkit","Data Format \u0026 I/O","Windows","Machine Learning Framework","Data Visualization","Linux","Time-Series \u0026 Financial","Debugging \u0026 Profiling \u0026 Tracing","Computer Vision","Data Management \u0026 Processing","DevOps","Web Development","Natural Language Processing","Process, Thread \u0026 Coroutine","Package Management","Security","Graphic Libraries \u0026 Renderers","Graph","MacOS","For JavaScript","CG Tutorials"],"sub_categories":["Flutter","JavaScript","For Scala","Data Management","C++/C Toolkit","For Java","Data Pre-processing \u0026 Loading","High-Level DL APIs","Others","General Purpose Tensor Library","For Python","General Purpose Framework","Deployment \u0026 Distribution","Classification \u0026 Detection \u0026 Tracking","C++/C","For C++/C","For JavaScript","Streaming Data Management","Data Representation","Python","Database \u0026 Cloud Management","Image / Video Generation","Python Toolkit","For Go","Experiment Management","General Purpose CV","Speech \u0026 Audio","Statistical Toolkit","Model Interpretation","General Purpose NLP","Tensor Similarity \u0026 Dimension Reduction","Hyperparameter Search \u0026 Gradient-Free Optimization","Conversation \u0026 Translation","Data Similarity","Nearest Neighbors \u0026 Similarity","Anomaly Detection \u0026 Others","OCR","Interpretability \u0026 Adversarial Training","Anomaly Detection","Java","Auto ML \u0026 Hyperparameter Optimization","Go","Scala"],"readme":"# Awesome List\n\n\u003ch1 align=\"center\"\u003e\n    \u003cbr\u003e\n    \u003cimg width=\"200\" src=\"awesome.svg\" alt=\"[!awesome](https://raw.githubusercontent.com/sindresorhus/awesome/main/media/logo.svg)\"\u003e\n    \u003cbr\u003e\n\u003c/h1\u003e\n\nA list of useful stuff in Machine Learning, Computer Graphics, Software Development, ...\n\n---\n\n# Table of Contents\n\n- [Machine Learning](#machine-learning)\n  - [Deep Learning Framework](#deep-learning-framework)\n    - [High-Level DL APIs](#high-level-dl-apis)\n    - [Deployment \u0026 Distribution](#deployment--distribution)\n    - [Auto ML \u0026 Hyperparameter Optimization](#auto-ml--hyperparameter-optimization)\n    - [Interpretability \u0026 Adversarial Training](#interpretability--adversarial-training)\n    - [Anomaly Detection \u0026 Others](#anomaly-detection--others)\n  - [Machine Learning Framework](#machine-learning-framework)\n    - [General Purpose Framework](#general-purpose-framework)\n    - [Nearest Neighbors \u0026 Similarity](#nearest-neighbors--similarity)\n    - [Hyperparameter Search \u0026 Gradient-Free Optimization](#hyperparameter-search--gradient-free-optimization)\n    - [Experiment Management](#experiment-management)\n    - [Model Interpretation](#model-interpretation)\n    - [Anomaly Detection](#anomaly-detection)\n  - [Computer Vision](#computer-vision)\n    - [General Purpose CV](#general-purpose-cv)\n    - [Classification \u0026 Detection \u0026 Tracking](#classification--detection--tracking)\n    - [OCR](#ocr)\n    - [Image / Video Generation](#image--video-generation)\n  - [Natural Language Processing](#natural-language-processing)\n    - [General Purpose NLP](#general-purpose-nlp)\n    - [Conversation \u0026 Translation](#conversation--translation)\n    - [Speech \u0026 Audio](#speech--audio)\n    - [Others](#others)\n  - [Reinforcement Learning](#reinforcement-learning)\n  - [Graph](#graph)\n  - [Causal Inference](#causal-inference)\n  - [Recommendation, Advertisement \u0026 Ranking](#recommendation-advertisement--ranking)\n  - [Time-Series \u0026 Financial](#time-series--financial)\n  - [Other Machine Learning Applications](#other-machine-learning-applications)\n  - [Linear Algebra / Statistics Toolkit](#linear-algebra--statistics-toolkit)\n    - [General Purpose Tensor Library](#general-purpose-tensor-library)\n    - [Tensor Similarity \u0026 Dimension Reduction](#tensor-similarity--dimension-reduction)\n    - [Statistical Toolkit](#statistical-toolkit)\n    - [Others](#others-1)\n  - [Data Processing](#data-processing)\n    - [Data Representation](#data-representation)\n    - [Data Pre-processing \u0026 Loading](#data-pre-processing--loading)\n    - [Data Similarity](#data-similarity)\n    - [Data Management](#data-management)\n  - [Data Visualization](#data-visualization)\n  - [Machine Learning Tutorials](#machine-learning-tutorials)\n- [Computer Graphics](#computer-graphics)\n  - [Graphic Libraries \u0026 Renderers](#graphic-libraries--renderers)\n  - [Game Engines](#game-engines)\n  - [CG Tutorials](#cg-tutorials)\n- [Full-Stack Development](#full-stack-development)\n  - [DevOps](#devops)\n  - [Desktop App Development](#desktop-app-development)\n    - [Python Toolkit](#python-toolkit)\n    - [C++/C Toolkit](#cc-toolkit)\n  - [Web Development](#web-development)\n  - [Mobile Development](#mobile-development)\n  - [Process, Thread \u0026 Coroutine](#process-thread--coroutine)\n  - [Debugging \u0026 Profiling \u0026 Tracing](#debugging--profiling--tracing)\n    - [For Python](#for-python)\n    - [For C++/C](#for-cc)\n    - [For Go](#for-go)\n  - [Data Management \u0026 Processing](#data-management--processing)\n    - [Database \u0026 Cloud Management](#database--cloud-management)\n    - [Streaming Data Management](#streaming-data-management)\n  - [Data Format \u0026 I/O](#data-format--io)\n    - [For Python](#for-python-1)\n    - [For C++/C](#for-cc-1)\n    - [For Go](#for-go-1)\n    - [For Java](#for-java)\n  - [Security](#security)\n  - [Package Management](#package-management)\n    - [For Python](#for-python-2)\n    - [For C++/C](#for-cc-2)\n    - [For Scala](#for-scala)\n    - [For JavaScript](#for-javascript)\n  - [Containers \u0026 Language Extentions \u0026 Linting](#containers--language-extentions--linting)\n    - [For Python](#for-python-3)\n    - [For C++/C](#for-cc-3)\n    - [For Go](#for-go-2)\n    - [For Java](#for-java-1)\n    - [For Scala](#for-scala-1)\n  - [For JavaScript](#for-javascript-1)\n  - [Programming Language Tutorials](#programming-language-tutorials)\n    - [Python](#python)\n    - [C++/C](#cc)\n    - [Go](#go)\n    - [Java](#java)\n    - [Scala](#scala)\n    - [Flutter](#flutter)\n    - [JavaScript](#javascript)\n- [Useful Tools](#useful-tools)\n  - [MacOS](#macos)\n  - [Windows](#windows)\n  - [Linux](#linux)\n  - [Cross-Platform](#cross-platform)\n- [Other Awesome Lists](#other-awesome-lists)\n  - [Machine Learning](#machine-learning-1)\n  - [Computer Graphics](#computer-graphics-1)\n  - [Programming Language](#programming-language)\n\n---\n\n# Machine Learning\n\n## Deep Learning Framework\n\n### High-Level DL APIs\n\n* [PyTorch](https://github.com/pytorch/pytorch) - An open source deep learning framework by Facebook, with GPU and dynamic graph support.\n  * Supported platform: *Linux, Windows, MacOS, Android, iOS*\n  * Language API: *Python, C++, Java*\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [TorchVision](https://github.com/pytorch/vision) - Datasets, Transforms and Models specific to Computer Vision for PyTorch\n    * [TorchText](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP for PyTorch\n    * [TorchAudio](https://github.com/pytorch/audio) - Data manipulation and transformation for audio signal processing for PyTorch\n    * [TorchRec](https://github.com/pytorch/torchrec) - A PyTorch domain library built to provide common sparsity \u0026 parallelism primitives needed for large-scale recommender systems (RecSys).\n    * [TorchServe](https://github.com/pytorch/serve) - Serve, optimize and scale PyTorch models in production\n    * [TorchHub](https://github.com/pytorch/hub) - Model zoo for PyTorch\n    * [Ignite](https://github.com/pytorch/ignite) - High-level library to help with training and evaluating neural networks for PyTorch\n    * [Captum](https://github.com/pytorch/captum) - A model interpretability and understanding library for PyTorch\n    * [Glow](https://github.com/pytorch/glow) - Compiler for Neural Network hardware accelerators\n    * [BoTorch](https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch\n    * [TNT](https://github.com/pytorch/tnt) - A library for PyTorch training tools and utilities\n    * [TorchArrow](https://github.com/pytorch/torcharrow) - Common and composable data structures built on PyTorch Tensor for efficient batch data representation and processing in PyTorch model authoring\n    * [PyTorchVideo](https://github.com/facebookresearch/pytorchvideo) - A deep learning library for video understanding research, based on PyTorch\n    * [tensorboardX](https://github.com/lanpa/tensorboardX) - Tensorboard for pytorch (and chainer, mxnet, numpy, ...)\n    * [TorchMetrics](https://github.com/Lightning-AI/metrics) - Machine learning metrics for distributed, scalable PyTorch applications\n    * [Apex](https://github.com/NVIDIA/apex) - Tools for easy mixed precision and distributed training in Pytorch\n    * [HuggingFace Accelerate](https://github.com/huggingface/accelerate) - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision\n    * [PyTorch Metric Learning](https://github.com/KevinMusgrave/pytorch-metric-learning) - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible, written in PyTorch\n    * [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch\n    * [torch-optimizer](https://github.com/jettify/pytorch-optimizer) - Collection of optimizers for PyTorch compatible with optim module\n    * [PyTorch Sparse](https://github.com/rusty1s/pytorch_sparse) - PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations\n    * [PyTorch Scatter](https://github.com/rusty1s/pytorch_scatter) - PyTorch Extension Library of Optimized Scatter Operations\n    * [Torch-Struct](https://github.com/harvardnlp/pytorch-struct) - A library of tested, GPU implementations of core structured prediction algorithms for deep learning applications\n    * [torchinfo](https://github.com/TylerYep/torchinfo) - View model summaries in PyTorch\n    * [Torchshow](https://github.com/xwying/torchshow) - Visualize PyTorch tensors with a single line of code\n    * [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) - An easy to use PyTorch to TensorRT converter\n    * [Kaolin](https://github.com/NVIDIAGameWorks/kaolin) - A PyTorch Library for Accelerating 3D Deep Learning Research\n    * [higher](https://github.com/facebookresearch/higher) **(not actively updated)** - A pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps\n    \u003c/details\u003e\n\n* [TensorFlow](https://github.com/tensorflow/tensorflow) - An open source deep learning framework by Google, with GPU support.\n  * Supported platform: *Linux, Windows, MacOS, Android, iOS, Raspberry Pi, Web*\n  * Language API: *Python, C++, Java, JavaScript*\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit\n    * [TensorFlow Text](https://github.com/tensorflow/text) - A collection of text related classes and ops for TensorFlow\n    * [TensorFlow Recommenders](https://github.com/tensorflow/recommenders) - A library for building recommender system models using TensorFlow.\n    * [TensorFlow Ranking](https://github.com/tensorflow/ranking) - A library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.\n    * [TensorFlow Serving](https://github.com/tensorflow/serving) - A flexible, high-performance serving system for machine learning models based on TensorFlow\n    * [TFX](https://github.com/tensorflow/tfx) - An end-to-end platform for deploying production ML pipelines.\n    * [TFDS](https://github.com/tensorflow/datasets) - A collection of datasets ready to use with TensorFlow and Jax\n    * [TensorFlow Addons](https://github.com/tensorflow/addons) - Useful extra functionality for TensorFlow 2.x maintained by SIG-addons\n    * [TensorFlow Transform](https://github.com/tensorflow/transform) - A library for preprocessing data with TensorFlow\n    * [TensorFlow Model Garden](https://github.com/tensorflow/models) - Models and examples built with TensorFlow\n    * [TensorFlow Hub](https://github.com/tensorflow/hub) - A library for transfer learning by reusing parts of TensorFlow models\n    * [TensorFlow.js](https://github.com/tensorflow/tfjs) - A WebGL accelerated JavaScript library for training and deploying ML models based on TensorFlow\n    * [TensorFlow Probability](https://github.com/tensorflow/probability) - Probabilistic reasoning and statistical analysis in TensorFlow\n    * [TensorFlow Model Optimization Toolkit](https://github.com/tensorflow/model-optimization) - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning\n    * [TensorFlow Model Analysis](https://github.com/tensorflow/model-analysis) - A library for evaluating TensorFlow models\n    * [Trax](https://github.com/google/trax) **(successor of Tensor2Tensor)** - Deep Learning with Clear Code and Speed\n    * [Lattice](https://github.com/tensorflow/lattice) - Lattice methods in TensorFlow\n    * [tf_numpy](https://www.tensorflow.org/guide/tf_numpy) - A subset of the NumPy API implemented in TensorFlow\n    * [TensorFlowOnSpark](https://github.com/yahoo/TensorFlowOnSpark) - Brings TensorFlow programs to Apache Spark clusters\n    * [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) **(no longer maintained)** - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research\n    \u003c/details\u003e\n\n* [MXNet](https://github.com/apache/incubator-mxnet) - An open source deep learning framework by Apache, with GPU support.\n  * Supported platform: *Linux, Windows, MacOS, Raspberry Pi*\n  * Language API: *Python, C++, R, Julia, Scala, Go, Javascript*\n\n* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - An open source deep learning framework by Baidu, with GPU support.\n  * Supported platform: *Linux, Windows, MacOS, Android, iOS, Web*\n  * Language API: *Python, C++, Java, JavaScript*\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) - Multilingual OCR toolkits based on PaddlePaddle\n    * [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) - Object detection toolkit based on PaddlePaddle\n    * [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) - Image segmentation toolkit based on PaddlePaddle\n    * [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) - Visual classification and recognition toolkit based on PaddlePaddle\n    * [PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN) - Generative Adversarial Networks toolkit based on PaddlePaddle\n    * [PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo) - Video understanding toolkit based on PaddlePaddle\n    * [PaddleRec](https://github.com/PaddlePaddle/PaddleRec) - Recommendation algorithm based on PaddlePaddle\n    * [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) - Natural language processing toolkit based on PaddlePaddle\n    * [PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech) - Speech Recognition/Translation toolkit based on PaddlePaddle\n    * [PGL](https://github.com/PaddlePaddle/PGL) - An efficient and flexible graph learning framework based on PaddlePaddle\n    * [PARL](https://github.com/PaddlePaddle/PARL) - A high-performance distributed training framework for Reinforcement Learning based on PaddlePaddle\n    * [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) - Pre-trained models toolkit based on PaddlePaddle\n    * [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) - Multi-platform high performance deep learning inference engine for PaddlePaddle\n    * [Paddle.js](https://github.com/PaddlePaddle/Paddle.js) - An open source deep learning framework running in the browser based on PaddlePaddle\n    * [VisualDL](https://github.com/PaddlePaddle/VisualDL) - A visualization analysis tool of PaddlePaddle\n    \u003c/details\u003e\n\n* [MegEngine](https://github.com/MegEngine/MegEngine) - An open source deep learning framework by MEGVII, with GPU support.\n  * Supported platform: *Linux, Windows, MacOS*\n  * Language API: *Python, C++*\n\n* [MACE](https://github.com/XiaoMi/mace) - A deep learning inference framework optimized for mobile heterogeneous computing by XiaoMi.\n  * Supported platform: *Android, iOS, Linux and Windows*\n\n* [Neural Network Libraries](https://github.com/sony/nnabla) - An open source deep learning framework by Sony, with GPU support.\n\n* [OneFlow](https://github.com/Oneflow-Inc/oneflow) - A deep learning framework designed to be user-friendly, scalable and efficient.\n\n* [fastai](https://github.com/fastai/fastai) - A high-level deep learning library based on PyTorch.\n\n* [Lightning](https://github.com/Lightning-AI/lightning) - A high-level deep learning library based on PyTorch.\n\n* [Lightning Flash](https://github.com/Lightning-AI/lightning-flash) - Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains\n\n* [tinygrad](https://github.com/geohot/tinygrad) - A deep learning framework in between a pytorch and a karpathy/micrograd.\n\n* [Flashlight](https://github.com/flashlight/flashlight) **(successor of wav2letter++)** - A C++ standalone library for machine learning.\n\n* [Avalanche](https://github.com/ContinualAI/avalanche) - An End-to-End Library for Continual Learning, based on PyTorch.\n\n* [ktrain](https://github.com/amaiya/ktrain) - A high-level deep learning library based on TensorFlow.\n\n* [Thinc](https://github.com/explosion/thinc) - A high-level deep learning library for PyTorch, TensorFlow and MXNet.\n\n* [Ludwig](https://github.com/ludwig-ai/ludwig) - A declarative deep learning framework that allows users to train, evaluate, and deploy models without the need to write code.\n\n* [Jina](https://github.com/jina-ai/jina) - A high-level deep learning library for serving and deployment.\n\n* [Haiku](https://github.com/deepmind/dm-haiku) - A high-level deep learning library based on JAX.\n\n* [scarpet-nn](https://github.com/ashutoshbsathe/scarpet-nn) - Tools and libraries to run neural networks in Minecraft.\n\n* [CNTK](https://github.com/microsoft/CNTK) **(not actively updated)** - An open source deep learning framework by Microsoft, with GPU support.\n  * Supported platform: *Linux, Windows*\n  * Language API: *Python, C++, Java, C#, .Net*\n\n* [DyNet](https://github.com/clab/dynet) **(not actively updated)** - A C++ deep learning library by CMU.\n  * Supported platform: *Linux, Windows, MacOS*\n  * Language API: *C++, Python*\n\n* [Chainer](https://github.com/chainer/chainer) **(not actively updated)** - A flexible framework of neural networks for deep learning.\n\n* [skorch](https://github.com/skorch-dev/skorch) **(not actively updated)** - A scikit-learn compatible neural network library based on PyTorch.\n\n* [MMF](https://github.com/facebookresearch/mmf) **(not actively updated)** - A modular framework for vision and language multimodal research by Facebook AI Research, based on PyTorch.\n\n* [Tensorpack](https://github.com/tensorpack/tensorpack) **(not actively updated)** - A high-level deep learning library based on TensorFlow.\n\n* [Sonnet](https://github.com/deepmind/sonnet) **(not actively updated)** - A high-level deep learning library based on TensorFlow.\n\n* [Ivy](https://github.com/unifyai/ivy) **(not actively updated)** - A high-level deep learning library that unifies NumPy, PyTorch, TensorFlow, MXNet and JAX.\n\n* [X-DeepLearning](https://github.com/alibaba/x-deeplearning) **(not actively updated)** - An industrial deep learning framework for high-dimension sparse data.\n\n* [HiddenLayer](https://github.com/waleedka/hiddenlayer) **(not actively updated)** - Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.\n\n* [TensorFX](https://github.com/TensorLab/tensorfx) **(not actively updated)** - TensorFlow framework for training and serving machine learning models.\n\n* [FeatherCNN](https://github.com/Tencent/FeatherCNN) **(not actively updated)** - A high performance inference engine for convolutional neural networks.\n\n* [tiny-dnn](https://github.com/tiny-dnn/tiny-dnn) **(not actively updated)** - Header only, dependency-free deep learning framework in C++14.\n\n* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow.\n\n### Deployment \u0026 Distribution\n\n* [MediaPipe](https://github.com/google/mediapipe) - Cross-platform, customizable ML solutions for live and streaming media.\n\n* [Triton](https://github.com/openai/triton) - A language and compiler for writing highly efficient custom Deep-Learning primitives.\n\n* [Hummingbird](https://github.com/microsoft/hummingbird) - A library for compiling trained traditional ML models into tensor computations.\n\n* [OpenVINO](https://github.com/openvinotoolkit/openvino) - An open-source toolkit for optimizing and deploying AI inference.\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo) - Pre-trained Deep Learning models and demos (high quality and extremely fast).\n    \u003c/details\u003e\n\n* [Kubeflow](https://github.com/kubeflow/kubeflow) - Machine Learning Toolkit for Kubernetes.\n\n* [Kubeflow Training Operator](https://github.com/kubeflow/training-operator) - Training operators on Kubernetes.\n\n* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies.\n\n* [DeepSpeed](https://github.com/microsoft/DeepSpeed) - An easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference.\n\n* [Analytics Zoo](https://github.com/intel-analytics/analytics-zoo) **(no longer maintained)** - Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink \u0026 Ray.\n\n* [BigDL](https://github.com/intel-analytics/BigDL) **(successor of Analytics Zoo)** - Building Large-Scale AI Applications for Distributed Big Data.\n\n* [FairScale](https://github.com/facebookresearch/fairscale) - A PyTorch extension library for high performance and large scale training.\n\n* [ColossalAI](https://github.com/hpcaitech/ColossalAI) - Provides a collection of parallel components and user-friendly tools to kickstart distributed training and inference in a few lines.\n\n* [Ray](https://github.com/ray-project/ray) - A unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.\n\n* [BentoML](https://github.com/bentoml/BentoML) - BentoML is compatible across machine learning frameworks and standardizes ML model packaging and management for your team.\n\n* [cortex](https://github.com/cortexlabs/cortex) - Production infrastructure for machine learning at scale.\n\n* [Horovod](https://github.com/horovod/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.\n\n* [Angel](https://github.com/Angel-ML/angel) - A Flexible and Powerful Parameter Server for large-scale machine learning.\n\n* [Elephas](https://github.com/maxpumperla/elephas) **(no longer maintained)** - Distributed Deep learning with Keras \u0026 Spark.\n\n* [Elephas](https://github.com/danielenricocahall/elephas) **(successor of maxpumperla/elephas)** - Distributed Deep learning with Keras \u0026 Spark.\n\n* [MLeap](https://github.com/combust/mleap) - Allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine.\n\n* [ZenML](https://github.com/zenml-io/zenml) - Build portable, production-ready MLOps pipelines.\n\n* [Optimus](https://github.com/hi-primus/optimus) - An opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark.\n\n* [ONNX](https://github.com/onnx/onnx) - Open standard for machine learning interoperability.\n\n* [TensorRT](https://github.com/NVIDIA/TensorRT) - A C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.\n\n* [Compute Library](https://github.com/ARM-software/ComputeLibrary) - A set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.\n\n* [Apache TVM](https://github.com/apache/tvm) - Open deep learning compiler stack for cpu, gpu and specialized accelerators.\n\n* [Triton Inference Server](https://github.com/triton-inference-server/server) - The Triton Inference Server provides an optimized cloud and edge inferencing solution.\n\n* [Core ML Tools](https://github.com/apple/coremltools) - Contains supporting tools for Core ML model conversion, editing, and validation.\n\n* [Petastorm](https://github.com/uber/petastorm) - Enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format.\n\n* [Hivemind](https://github.com/learning-at-home/hivemind) - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.\n\n* [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax) - Model parallel transformers in JAX and Haiku.\n\n* [Nebullvm](https://github.com/nebuly-ai/nebullvm) - An open-source tool designed to speed up AI inference in just a few lines of code.\n\n* [ncnn](https://github.com/Tencent/ncnn) - A high-performance neural network inference framework optimized for the mobile platform.\n\n* [Turi Create](https://github.com/apple/turicreate) **(not actively updated)** - A machine learning library for deployment on MacOS/iOS.\n\n* [Apache SINGA](https://github.com/apache/singa) **(not actively updated)** - A distributed deep learning platform.\n\n* [BytePS](https://github.com/bytedance/byteps) **(not actively updated)** - A high performance and generic framework for distributed DNN training.\n\n* [MMdnn](https://github.com/microsoft/MMdnn) **(not actively updated)** - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks.\n\n### Auto ML \u0026 Hyperparameter Optimization\n\n* [NNI](https://github.com/microsoft/nni) - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.\n\n* [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for deep learning.\n\n* [KerasTuner](https://github.com/keras-team/keras-tuner) - An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.\n\n* [Talos](https://github.com/autonomio/talos) - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.\n\n* [Distiller](https://github.com/IntelLabs/distiller) - Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research.\n\n* [Hyperas](https://github.com/maxpumperla/hyperas) **(not actively updated)** - A very simple wrapper for convenient hyperparameter optimization for Keras.\n\n* [Model Search](https://github.com/google/model_search) **(not actively updated)** - A framework that implements AutoML algorithms for model architecture search at scale.\n\n### Interpretability \u0026 Adversarial Training\n\n* [AI Explainability 360](https://github.com/Trusted-AI/AIX360) - An open-source library that supports interpretability and explainability of datasets and machine learning models.\n\n* [explainerdashboard](https://github.com/oegedijk/explainerdashboard) - Quickly build Explainable AI dashboards that show the inner workings of so-called \"blackbox\" machine learning models.\n\n* [iNNvestigate](https://github.com/albermax/innvestigate) - A toolbox to innvestigate neural networks' predictions.\n\n* [Foolbox](https://github.com/bethgelab/foolbox) - A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX.\n\n* [AdvBox](https://github.com/advboxes/AdvBox) - A toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow.\n\n* [Adversarial Robustness Toolbox](https://github.com/Trusted-AI/adversarial-robustness-toolbox) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference.\n\n* [CleverHans](https://github.com/cleverhans-lab/cleverhans) - An adversarial example library for constructing attacks, building defenses, and benchmarking both.\n\n### Anomaly Detection \u0026 Others\n\n* [Anomalib](https://github.com/openvinotoolkit/anomalib) - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.\n\n* [Gradio](https://github.com/gradio-app/gradio) - An open-source Python library that is used to build machine learning and data science demos and web applications.\n\n* [Traingenerator](https://github.com/jrieke/traingenerator) - Generates custom template code for PyTorch \u0026 sklearn, using a simple web UI built with streamlit.\n\n* [Fairlearn](https://github.com/fairlearn/fairlearn) - A Python package to assess and improve fairness of machine learning models.\n\n* [AI Fairness 360](https://github.com/Trusted-AI/AIF360) - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.\n\n## Machine Learning Framework\n\n### General Purpose Framework\n\n* [scikit-learn](https://github.com/scikit-learn/scikit-learn) - Machine learning toolkit for Python.\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - A python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance\n    * [category_encoders](https://github.com/scikit-learn-contrib/category_encoders) - A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques\n    * [lightning](https://github.com/scikit-learn-contrib/lightning) - Large-scale linear classification, regression and ranking in Python\n    * [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - Pandas integration with sklearn\n    * [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) - A high performance implementation of HDBSCAN clustering\n    * [metric-learn](https://github.com/scikit-learn-contrib/metric-learn) - Metric learning algorithms in Python\n    * [scikit-optimize](https://github.com/scikit-optimize/scikit-optimize) - Sequential model-based optimization with a `scipy.optimize` interface\n    * [scikit-image](https://github.com/scikit-image/scikit-image) - Image processing in Python\n    * [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.\n    * [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) - A Python module capable of performing multi-label learning tasks\n    * [scikit-lego](https://github.com/koaning/scikit-lego) - Extra blocks for scikit-learn pipelines.\n    * [scikit-opt](https://github.com/guofei9987/scikit-opt) - Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)\n    * [sklearn-porter](https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript and others.\n  \u003c/details\u003e\n\n* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.\n  * Supported platform: *Linux, Windows, MacOS*\n  * Supported distributed framework: *Hadoop, Spark, Dask, Flink, DataFlow*\n  * Language API: *Python, C++, R, Java, Scala, Go*\n\n* [LightGBM](https://github.com/microsoft/LightGBM) - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms.\n  * Supported platform: *Linux, Windows, MacOS*\n  * Language API: *Python, C++, R*\n\n* [CatBoost](https://github.com/catboost/catboost) - A fast, scalable, high performance Gradient Boosting on Decision Trees library.\n  * Supported platform: *Linux, Windows, MacOS*\n  * Language API: *Python, C++, R, Java*\n\n* [Autograd](https://github.com/HIPS/autograd) **(no longer maintained)** - Efficiently computes derivatives of numpy code.\n\n* [JAX](https://github.com/google/jax) **(successor of Autograd)** - Automatical differentiation for native Python and NumPy functions, with GPU support.\n\n* [Flax](https://github.com/google/flax) - A high-performance neural network library and ecosystem for JAX that is designed for flexibility.\n\n* [Equinox](https://github.com/patrick-kidger/equinox) - A JAX library based around a simple idea: represent parameterised functions (such as neural networks) as PyTrees.\n\n* [cuML](https://github.com/rapidsai/cuml) - A suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.\n\n* [Mlxtend](https://github.com/rasbt/mlxtend) - A library of extension and helper modules for Python's data analysis and machine learning libraries.\n\n* [River](https://github.com/online-ml/river) - A Python library for online machine learning.\n\n* [FilterPy](https://github.com/rlabbe/filterpy) - Python Kalman filtering and optimal estimation library.\n\n* [igel](https://github.com/nidhaloff/igel) - A delightful machine learning tool that allows you to train, test, and use models without writing code.\n\n* [fklearn](https://github.com/nubank/fklearn) - A machine learning library that uses functional programming principles.\n\n* [SynapseML](https://github.com/microsoft/SynapseML) - An open-source library that simplifies the creation of massively scalable machine learning pipelines.\n\n* [Dask](https://github.com/dask/dask) - A flexible parallel computing library for NumPy, Pandas and Scikit-Learn.\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [Distributed](https://github.com/dask/distributed) - A distributed task scheduler for Dask\n  \u003c/details\u003e\n\n* [H2O](https://github.com/h2oai/h2o-3) - An in-memory platform for distributed, scalable machine learning.\n\n* [autodiff](https://github.com/autodiff/autodiff) - automatic differentiation made easier for C++\n\n* [GoLearn](https://github.com/sjwhitworth/golearn) - Machine Learning for Go.\n\n* [leaves](https://github.com/dmitryikh/leaves) - Pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks.\n\n* [go-xgboost](https://github.com/Unity-Technologies/go-xgboost) - XGBoost bindings for golang.\n\n* [DEAP](https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python.\n\n* [ESTool](https://github.com/hardmaru/estool) - Evolution Strategies Tool.\n\n* [mlpack](https://github.com/mlpack/mlpack) **(not actively updated)** - A header-only C++ machine learning library.\n  * Language API: *C++, Python, R, Julia, Go*\n\n* [xLearn](https://github.com/aksnzhy/xlearn) **(not actively updated)** - A C++ machine learning library for linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM).\n\n* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) **(not actively updated)** - Fast GBDTs and Random Forests on GPUs.\n\n* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) **(not actively updated)** - A Fast SVM Library on GPUs and CPUs.\n\n* [PyBrain](https://github.com/pybrain/pybrain) - The Python Machine Learning Library.\n\n### Nearest Neighbors \u0026 Similarity\n\n* [Annoy](https://github.com/spotify/annoy) - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk.\n\n* [Hnswlib](https://github.com/nmslib/hnswlib) - Header-only C++/python library for fast approximate nearest neighbors.\n\n* [NMSLIB](https://github.com/nmslib/nmslib) - Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.\n\n* [ann-benchmarks](https://github.com/erikbern/ann-benchmarks) - Benchmarks of approximate nearest neighbor libraries in Python.\n\n* [kmodes](https://github.com/nicodv/kmodes) - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data.\n\n### Hyperparameter Search \u0026 Gradient-Free Optimization\n\n* [Optuna](https://github.com/optuna/optuna) - An automatic hyperparameter optimization software framework, particularly designed for machine learning.\n\n* [Ax](https://github.com/facebook/Ax) - An accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.\n\n* [AutoGluon](https://github.com/awslabs/autogluon) - Automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.\n\n* [Nevergrad](https://github.com/facebookresearch/nevergrad) - A Python toolbox for performing gradient-free optimization.\n\n* [MLJAR](https://github.com/mljar/mljar-supervised) - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation.\n\n* [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python, with a scikit-learn inspired API.\n\n* [BayesianOptimization](https://github.com/fmfn/BayesianOptimization) **(not actively updated)** - A Python implementation of global optimization with gaussian processes.\n\n* [Hyperopt](https://github.com/hyperopt/hyperopt) **(not actively updated)** - Distributed Asynchronous Hyperparameter Optimization in Python.\n\n* [Dragonfly](https://github.com/dragonfly/dragonfly) **(not actively updated)** - An open source python library for scalable Bayesian optimization.\n\n### Experiment Management\n\n* [MLflow](https://github.com/mlflow/mlflow) - A platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.\n\n* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python that automates machine learning workflows.\n\n* [Aim](https://github.com/aimhubio/aim) - An open-source, self-hosted ML experiment tracking tool.\n\n* [Ax](https://github.com/facebook/Ax) - An accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.\n\n* [labml](https://github.com/labmlai/labml) - Monitor deep learning model training and hardware usage from your mobile phone.\n\n* [ClearML](https://github.com/allegroai/clearml) - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management.\n\n* [DVC](https://github.com/iterative/dvc) - A command line tool and VS Code Extension for data/model version control.\n\n* [Metaflow](https://github.com/Netflix/metaflow) - A human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects.\n\n* [Weights\u0026Biases](https://github.com/wandb/wandb) - A tool for visualizing and tracking your machine learning experiments.\n\n* [Yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection.\n\n### Model Interpretation\n\n* [dtreeviz](https://github.com/parrt/dtreeviz) - A python library for decision tree visualization and model interpretation.\n\n* [InterpretML](https://github.com/interpretml/interpret) - An open-source package that incorporates state-of-the-art machine learning interpretability techniques.\n\n* [Shapash](https://github.com/MAIF/shapash) - A Python library which aims to make machine learning interpretable and understandable by everyone.\n\n* [Alibi](https://github.com/SeldonIO/alibi) - An open source Python library aimed at machine learning model inspection and interpretation.\n\n* [PyCM](https://github.com/sepandhaghighi/pycm) - Multi-class confusion matrix library in Python.\n\n### Anomaly Detection\n\n* [PyOD](https://github.com/yzhao062/pyod) - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection).\n\n* [Alibi Detect](https://github.com/SeldonIO/alibi-detect) - Algorithms for outlier, adversarial and drift detection.\n\n## Computer Vision\n\n### General Purpose CV\n\n* [OpenCV](https://github.com/opencv/opencv) - Open Source Computer Vision Library.\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [opencv-python](https://github.com/opencv/opencv-python) - Pre-built CPU-only OpenCV packages for Python.\n    * [opencv_contrib](https://github.com/opencv/opencv_contrib) - Repository for OpenCV's extra modules.\n    * [CVAT](https://github.com/opencv/cvat) - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.\n  \u003c/details\u003e\n\n* [OMMCV](https://github.com/open-mmlab/mmcv) - OpenMMLab Computer Vision Foundation.\n  * \u003cdetails open\u003e\u003csummary\u003eRelated projects:\u003c/summary\u003e\n\n    * [MMClassification](https://github.com/open-mmlab/mmclassification) - OpenMMLab Image Classification Toolbox and Benchmark\n    * [MMDetection](https://github.com/open-mmlab/mmdetection) - OpenMMLab Detection Toolbox and Benchmark\n    * [MMDetection3D](https://github.com/open-mmlab/mmdetection3d) - OpenMMLab's next-generation platform for general 3D object detection\n    * [MMOCR](https://github.com/open-mmlab/mmocr) - OpenMMLab Text Detection, Recognition and Understanding Toolbox\n    * [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) - OpenMMLab Semantic Segmentation Toolbox and Benchmark.\n    * [MMTracking](https://github.com/open-mmlab/mmtracking) - OpenMMLab Video Perception Toolbox\n    * [MMPose](https://github.com/open-mmlab/mmpose) - OpenMMLab Pose Estimation Toolbox and Benchmark\n    * [MMSkeleton](https://github.com/open-mmlab/mmskeleton) - A OpenMMLAB toolbox for human pose estimation, skeleton-based action recognition, and action synthesis\n    * [MMGeneration](https://github.com/open-mmlab/mmgeneration) - MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV\n    * [MMEditing](https://github.com/open-mmlab/mmediting) - MMEditing is a low-level vision toolbox based on PyTorch, supporting super-resolution, inpainting, matting, video interpolation, etc\n    * [MMDeploy](https://github.com/open-mmlab/mmdeploy) - OpenMMLab Model Deployment Framework\n    * [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) - OpenPCDet Toolbox for LiDAR-based 3D Object Detection\n  \u003c/details\u003e\n\n* [Lightly](https://github.com/lightly-ai/lightly) - A computer vision framework for self-supervised learning, based on PyTorch.\n\n* [GluonCV](https://github.com/dmlc/gluon-cv) - A high-level computer vision library for PyTorch and MXNet.\n\n* [Scenic](https://github.com/google-research/scenic) - A codebase with a focus on research around attention-based models for computer vision, based on JAX and Flax.\n\n* [Kornia](https://github.com/kornia/kornia) - Open source differentiable computer vision library, based on PyTorch.\n\n* [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) - A collection of CV models, scripts, pretrained weights, based on PyTorch.\n\n* [vit-pytorch](https://github.com/lucidrains/vit-pytorch) - A collection of Vision Transformer implementations, based on PyTorch.\n\n* [vit-tensorflow](https://github.com/taki0112/vit-tensorflow) - A collection of Vision Transformer implementations, based on TensorFlow.\n\n* [ccv](https://github.com/liuliu/ccv) - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.\n\n* [TorchCV](https://github.com/donnyyou/torchcv) **(not actively updated)** - A PyTorch-Based Framework for Deep Learning in Computer Vision.\n\n### Classification \u0026 Detection \u0026 Tracking\n\n* [Detectron](https://github.com/facebookresearch/Detectron/) **(no longer maintained)** - A research platform for object detection research, implementing popular algorithms by Facebook, based on Caffe2.\n\n* [Detectron2](https://github.com/facebookresearch/detectron2) **(successor of Detectron)** - A platform for object detection, segmentation and other visual recognition tasks, based on PyTorch.\n\n* [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) - Real-Time and Accurate Full-Body Multi-Person Pose Estimation\u0026Tracking System.\n\n* [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) - Real-time multi-person keypoint detection library for body, face, hands, and foot estimation.\n\n* [OpenPose Unity Plugin](https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin) - A wrapper of the OpenPose library for Unity users.\n\n* [Norfair](https://github.com/tryolabs/norfair) - Lightweight Python library for adding real-time multi-object tracking to any detector.\n\n* [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet) - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet ).\n\n* [pjreddie/darknet](https://github.com/pjreddie/darknet) - Convolutional Neural Networks.\n\n* [ClassyVision](https://github.com/facebookresearch/ClassyVision) - An end-to-end framework for image and video classification, based on PyTorch.\n\n* [pycls](https://github.com/facebookresearch/pycls) - Codebase for Image Classification Research, based on PyTorch.\n\n* [CenterNet](https://github.com/xingyizhou/CenterNet) - Object detection, 3D detection, and pose estimation using center point detection.\n\n* [SlowFast](https://github.com/facebookresearch/SlowFast) - Video understanding codebase from FAIR, based on PyTorch.\n\n* [SAHI](https://github.com/obss/sahi) - Platform agnostic sliced/tiled inference + interactive ui + error analysis plots for object detection and instance segmentation.\n\n* [libfacedetection](https://github.com/ShiqiYu/libfacedetection) - An open source library for face detection in images. The face detection speed can reach 1000FPS.\n\n* [openbr](https://github.com/biometrics/openbr) - Open Source Biometrics, Face Recognition.\n\n* [InsightFace](https://github.com/deepinsight/insightface) - An open source 2D\u00263D deep face analysis toolbox, based on PyTorch and MXNet.\n\n* [Deepface](https://github.com/serengil/deepface) - A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python.\n\n* [deepfakes_faceswap](https://github.com/deepfakes/faceswap) - A tool that utilizes deep learning to recognize and swap faces in pictures and videos.\n\n* [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) - 1MB lightweight face detection model.\n\n* [face_classification](https://github.com/oarriaga/face_classification) **(no longer maintained)** - Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.\n\n* [PAZ](https://github.com/oarriaga/paz) **(successor of face_classification)** - Hierarchical perception library in Python for pose estimation, object detection, instance segmentation, keypoint estimation, face recognition, etc.\n\n* [MenpoBenchmark](https://github.com/jiankangdeng/MenpoBenchmark) - Multi-pose 2D and 3D Face Alignment \u0026 Tracking.\n\n* [CaImAn](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.\n\n* [segmentation_models](https://github.com/qubvel/segmentation_models) **(not actively updated)** - Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow.\n\n* [OpenFace](https://github.com/cmusatyalab/openface) **(not actively updated)** - Face recognition with deep neural networks.\n\n* [Face Recognition](https://github.com/ageitgey/face_recognition) **(not actively updated)** - A facial recognition api for Python and the command line.\n\n* [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) **(not actively updated)** - A state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.\n\n* [hgpvision/darknet](https://github.com/hgpvision/darknet) **(not actively updated)** - darknet深度学习框架源码分析：详细中文注释，涵盖框架原理与实现语法分析\n\n### OCR\n\n* [EasyOCR](https://github.com/JaidedAI/EasyOCR) - Ready-to-use OCR with 80+ supported languages and all popular writing scripts.\n\n* [Python-tesseract](https://github.com/madmaze/pytesseract) - A Python wrapper for Google's Tesseract-OCR Engine.\n\n* [tesserocr](https://github.com/sirfz/tesserocr) - A simple, Pillow-friendly, wrapper around the tesseract-ocr API for OCR.\n\n* [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark) - Text recognition (optical character recognition) with deep learning methods.\n\n* [OCRmyPDF](https://github.com/ocrmypdf/OCRmyPDF) - Adds an OCR text layer to scanned PDF files, allowing them to be searched.\n\n* [LayoutParser](https://github.com/Layout-Parser/layout-parser) - A Unified Toolkit for Deep Learning Based Document Image Analysis, based on Detectron2.\n\n* [chineseocr](https://github.com/chineseocr/chineseocr) - yolo3+ocr\n\n* [HyperLPR](https://github.com/szad670401/HyperLPR) - 基于深度学习高性能中文车牌识别\n\n* [deep_ocr](https://github.com/JinpengLI/deep_ocr) **(not actively updated)** - make a better chinese character recognition OCR than tesseract\n\n* [chinese_ocr](https://github.com/YCG09/chinese_ocr) **(not actively updated)** - CTPN + DenseNet + CTC based end-to-end Chinese OCR implemented using tensorflow and keras.\n\n* [pdftabextract](https://github.com/WZBSocialScienceCenter/pdftabextract) **(no longer maintained)** - A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.\n\n* [CHINESE-OCR](https://github.com/xiaofengShi/CHINESE-OCR) **(not actively updated)** - 运用tf实现自然场景文字检测,keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别\n\n* [EasyPR](https://github.com/liuruoze/EasyPR) - 一个开源的中文车牌识别系统\n\n* [License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9](https://github.com/zhubenfu/License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9) **(not actively updated)** - 中文车牌识别\n\n### Image / Video Generation\n\n* [DALL·E Flow](https://github.com/jina-ai/dalle-flow) - A Human-in-the-Loop workflow for creating HD images from text.\n\n* [DALL·E Mini](https://github.com/borisdayma/dalle-mini) - Generate images from a text prompt.\n\n* [GAN Lab](https://github.com/poloclub/ganlab) - An Interactive, Visual Experimentation Tool for Generative Adversarial Networks.\n\n* [DeepFaceLab](https://github.com/iperov/DeepFaceLab) - DeepFaceLab is the leading software for creating deepfakes.\n\n* [DeOldify](https://github.com/jantic/DeOldify) - A Deep Learning based project for colorizing and restoring old images (and video!)\n\n* [waifu2x](https://github.com/nagadomi/waifu2x) - Image Super-Resolution for Anime-Style Art.\n\n* [Kubric](https://github.com/google-research/kubric) - A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.\n\n* [benchmark_VAE](https://github.com/clementchadebec/benchmark_VAE) - Implements some of the most common (Variational) Autoencoder models under a unified implementation.\n\n* [FastPhotoStyle](https://github.com/NVIDIA/FastPhotoStyle) **(not actively updated)** - Style transfer, deep learning, feature transform.\n\n* [Real-Time-Person-Removal](https://github.com/jasonmayes/Real-Time-Person-Removal) **(not actively updated)** - Removing people from complex backgrounds in real time using TensorFlow.js in the web browser.\n\n* [MUNIT](https://github.com/NVlabs/MUNIT) **(no longer maintained)** - Multimodal Unsupervised Image-to-Image Translation.\n\n* [pytorch_GAN_zoo](https://github.com/facebookresearch/pytorch_GAN_zoo) **(not actively updated)** - A mix of GAN implementations including progressive growing.\n\n* [deepcolor](https://github.com/kvfrans/deepcolor) **(not actively updated)** - Automatic coloring and shading of manga-style lineart, using Tensorflow + cGANs.\n\n## Natural Language Processing\n\n### General Purpose NLP\n\n* [HuggingFace Transformers](https://github.com/huggingface/transformers) - A high-level machine learning library for text, images and audio data, with support for Pytorch, TensorFlow and JAX.\n\n* [HuggingFace Tokenizers](https://github.com/huggingface/tokenizers) - A high-performance library for text vocabularies and tokenizers.\n\n* [NLTK](https://github.com/nltk/nltk) - An open source natural language processing library in Python.\n\n* [spaCy](https://github.com/explosion/spaCy) - Industrial-strength Natural Language Processing (NLP) in Python.\n\n* [ScispaCy](https://github.com/allenai/scispacy) - A Python package containing spaCy models for processing biomedical, scientific or clinical text.\n\n* [PyTextRank](https://github.com/DerwenAI/pytextrank) - A Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work.\n\n* [textacy](https://github.com/chartbeat-labs/textacy) - a Python library for performing a variety of natural language processing tasks, based on spaCy.\n\n* [spacy-transformers](https://github.com/explosion/spacy-transformers) - Use pretrained transformers in spaCy, based on HuggingFace Transformers.\n\n* [Spark NLP](https://github.com/JohnSnowLabs/spark-nlp) - An open source natural language processing library for Apache Spark.\n\n* [Flair](https://github.com/flairNLP/flair) - An open source natural language processing library, based on PyTorch.\n\n* [Fairseq](https://github.com/facebookresearch/fairseq) - A sequence-to-sequence toolkit by Facebook, based on PyTorch.\n\n* [ParlAI](https://github.com/facebookresearch/ParlAI) - A python framework for sharing, training and testing dialogue models from open-domain chitchat, based on PyTorch.\n\n* [Stanza](https://github.com/stanfordnlp/stanza) - An open source natural language processing library by Stanford NLP Group, based on PyTorch.\n\n* [ESPnet](https://github.com/espnet/espnet) - An end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, based on PyTorch.\n\n* [NLP Architect](https://github.com/IntelLabs/nlp-architect) - A Deep Learning NLP/NLU library by Intel AI Lab, based on PyTorch and TensorFlow.\n\n* [LightSeq](https://github.com/bytedance/lightseq) - A high performance training and inference library for sequence processing and generation implemented in CUDA, for Fairseq and HuggingFace Transformers.\n\n* [FudanNLP](https://github.com/FudanNLP/fnlp) **(no longer maintained)** - Toolkit for Chinese natural language processing.\n\n* [fastNLP](https://github.com/fastnlp/fastNLP) **(successor of FudanNLP)** - A Modularized and Extensible NLP Framework for PyTorch and PaddleNLP.\n\n* [Rubrix](https://github.com/recognai/rubrix) - A production-ready Python framework for exploring, annotating, and managing data in NLP projects.\n\n* [Gensim](https://github.com/RaRe-Technologies/gensim) - A Python library for topic modelling, document indexing and similarity retrieval with large corpora, based on NumPy and SciPy.\n\n* [CLTK](https://github.com/cltk/cltk) - A Python library offering natural language processing for pre-modern languages.\n\n* [OpenNRE](https://github.com/thunlp/OpenNRE) - An open-source and extensible toolkit that provides a unified framework to implement relation extraction models.\n\n* [minGPT](https://github.com/karpathy/minGPT) - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training.\n\n* [HanLP](https://github.com/hankcs/HanLP) - 中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理\n\n* [LAC](https://github.com/baidu/lac) - 百度NLP：分词，词性标注，命名实体识别，词重要性\n\n* [AllenNLP](https://github.com/allenai/allennlp) **(not actively updated)** - An open source natural language processing library, based on PyTorch.\n\n* [GluonNLP](https://github.com/dmlc/gluon-nlp) **(not actively updated)** - A high-level NLP toolkit, based on MXNet.\n\n* [jiant](https://github.com/nyu-mll/jiant) **(no longer maintained)** - The multitask and transfer learning toolkit for natural language processing research.\n\n* [fastText](https://github.com/facebookresearch/fastText) **(not actively updated)** - A library for efficient learning of word representations and sentence classification.\n\n* [TextBlob](https://github.com/sloria/TextBlob) **(not actively updated)** - A Python library for processing textual data.\n\n* [jieba](https://github.com/fxsjy/jieba) **(not actively updated)** - 结巴中文分词\n\n* [SnowNLP](https://github.com/isnowfy/snownlp) **(not actively updated)** - Python library for processing Chinese text.\n\n### Conversation \u0026 Translation\n\n* [SpeechBrain](https://github.com/speechbrain/speechbrain) - An open-source and all-in-one conversational AI toolkit based on PyTorch.\n\n* [NeMo](https://github.com/NVIDIA/NeMo) - A toolkit for conversational AI, based on PyTorch.\n\n* [Sockeye](https://github.com/awslabs/sockeye) - An open-source sequence-to-sequence framework for Neural Machine Translation, based on PyTorch.\n\n* [DeepPavlov](https://github.com/deeppavlov/DeepPavlov) - An open-source conversational AI library built on TensorFlow, Keras and PyTorch.\n\n* [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) - The PyTorch version of the OpenNMT project, an open-source neural machine translation framework.\n\n* [OpenNMT-tf](https://github.com/OpenNMT/OpenNMT-tf) - The TensorFlow version of the OpenNMT project, an open-source neural machine translation framework.\n\n* [Rasa](https://github.com/RasaHQ/rasa) - Open source machine learning framework to automate text- and voice-based conversations.\n\n* [SentencePiece](https://github.com/google/sentencepiece) - Unsupervised text tokenizer for Neural Network-based text generation.\n\n* [subword-nmt](https://github.com/rsennrich/subword-nmt) - Unsupervised Word Segmentation for Neural Machine Translation and Text Generation.\n\n* [OpenPrompt](https://github.com/thunlp/OpenPrompt) - An Open-Source Framework for Prompt-Learning.\n\n* [sumy](https://github.com/miso-belica/sumy) - Module for automatic summarization of text documents and HTML pages.\n\n* [chatbot](https://github.com/zhaoyingjun/chatbot) - 一个可以自己进行训练的中文聊天机器人， 根据自己的语料训练出自己想要的聊天机器人，可以用于智能客服、在线问答、智能聊天等场景。\n\n* [AI-Writer](https://github.com/BlinkDL/AI-Writer) - AI 写小说，生成玄幻和言情网文等等。中文预训练生成模型。\n\n* [seq2seq-couplet](https://github.com/wb14123/seq2seq-couplet) - 用深度学习对对联。\n\n* [FARM](https://github.com/deepset-ai/FARM) **(not actively updated)** - Fast \u0026 easy transfer learning for NLP, which focuses on Question Answering.\n\n* [Haystack](https://github.com/deepset-ai/haystack) **(successor of FARM)** - A high-level natural language processing library for deployment and production, based on PyTorch and HuggingFace Transformers.\n\n* [XLM](https://github.com/facebookresearch/XLM) **(not actively updated)** - PyTorch original implementation of Cross-lingual Language Model Pretraining.\n\n### Speech \u0026 Audio\n\n* [TTS](https://github.com/coqui-ai/TTS) - A library for advanced Text-to-Speech generation.\n\n* [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis) - A Python library for audio feature extraction, classification, segmentation and applications.\n\n* [Porcupine](https://github.com/Picovoice/porcupine) - On-device wake word detection powered by deep learning.\n\n* [MuseGAN](https://github.com/salu133445/musegan) - An AI for Music Generation.\n\n* [wav2letter++](https://github.com/flashlight/wav2letter) **(no longer maintained)** - Facebook AI Research's Automatic Speech Recognition Toolkit.\n\n* [Magenta](https://github.com/magenta/magenta) **(no longer maintained)** - Music and Art Generation with Machine Intelligence.\n\n* [SpeechRecognition](https://github.com/Uberi/speech_recognition) **(not actively updated)** - Library for performing speech recognition, with support for several engines and APIs, online and offline.\n\n### Others\n\n* [Spleeter](https://github.com/deezer/spleeter) - A source separation library with pretrained models, based on TensorFlow.\n\n* [Language Interpretability Tool](https://github.com/PAIR-code/lit) - Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.\n\n* [TextAttack](https://github.com/QData/TextAttack) - A Python framework for adversarial attacks, data augmentation, and model training in NLP.\n\n* [CheckList](https://github.com/marcotcr/checklist) - Behavioral Testing of NLP models with CheckList.\n\n## Reinforcement Learning\n\n* [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms by OpenAI.\n\n* [DeepMind Lab](https://github.com/deepmind/lab) - A customisable 3D platform for agent-based AI research.\n\n* [TF-Agents](https://github.com/tensorflow/agents) - A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.\n\n* [TensorLayer](https://github.com/tensorlayer/TensorLayer) - A novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers.\n\n* [Tensorforce](https://github.com/tensorforce/tensorforce) - A TensorFlow library for applied reinforcement learning.\n\n* [Acme](https://github.com/deepmind/acme) - A research framework for reinforcement learning by DeepMind.\n\n* [RLax](https://github.com/deepmind/rlax) - A library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents.\n\n* [ReAgent](https://github.com/facebookresearch/ReAgent) - An open source end-to-end platform for applied reinforcement learning by Facebook.\n\n* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.\n\n* [Vowpal Wabbit](https://github.com/VowpalWabbit/vowpal_wabbit) - A fast, flexible, online, and active learning solution for solving complex interactive machine learning problems.\n\n* [PFRL](https://github.com/pfnet/pfrl) - A PyTorch-based deep reinforcement learning library.\n\n* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.\n\n* [PyRobot](https://github.com/facebookresearch/pyrobot) - An Open Source Robotics Research Platform.\n\n* [AirSim](https://github.com/microsoft/AirSim) - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI \u0026 Research.\n\n* [Self-Driving-Car-in-Video-Games](https://github.com/ikergarcia1996/Self-Driving-Car-in-Video-Games) - A deep neural network that learns to drive in video games.\n\n* [OpenAI Baselines](https://github.com/openai/baselines) **(no longer maintained)** - A set of high-quality implementations of reinforcement learning algorithms.\n\n* [Stable Baselines](https://github.com/hill-a/stable-baselines) **(no longer maintained)** - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.\n\n* [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) **(successor of OpenAI Baselines and Stable Baselines)** - A set of reliable implementations of reinforcement learning algorithms in PyTorch.\n\n* [PySC2](https://github.com/deepmind/pysc2) - StarCraft II Learning Environment.\n\n* [ViZDoom](https://github.com/mwydmuch/ViZDoom) - Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information.\n\n* [FinRL](https://github.com/AI4Finance-Foundation/FinRL) - The first open-source framework to show the great potential of financial reinforcement learning.\n\n* [AnimalAI-Olympics](https://github.com/beyretb/AnimalAI-Olympics) **(no longer maintained)** - Code repository for the Animal AI Olympics competition.\n\n* [AnimalAI 3](https://github.com/mdcrosby/animal-ai) **(successor of AnimalAI-Olympics)** - AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition.\n\n* [self-driving-car](https://github.com/udacity/self-driving-car) **(no longer maintained)** - The Udacity open source self-driving car project.\n\n## Graph\n\n* [DGL](https://github.com/dmlc/dgl) - An easy-to-use, high performance and scalable Python package for deep learning on graphs for PyTorch, Apache MXNet or TensorFlow.\n\n* [NetworkX](https://github.com/networkx/networkx) - A Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n\n* [igraph](https://github.com/igraph/igraph) - Library for the analysis of networks.\n\n* [python-igraph](https://github.com/igraph/python-igraph) - Python interface for igraph.\n\n* [PyG](https://github.com/pyg-team/pytorch_geometric) - A Graph Neural Network Library based on PyTorch.\n\n* [PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) - Graph Neural Network Library for PyTorch.\n\n* [OGB](https://github.com/snap-stanford/ogb) - Benchmark datasets, data loaders, and evaluators for graph machine learning.\n\n* [Spektral](https://github.com/danielegrattarola/spektral) - A Python library for graph deep learning, based on Keras and TensorFlow.\n\n* [Graph Nets](https://github.com/deepmind/graph_nets) - Build Graph Nets in Tensorflow.\n\n* [Graph4nlp](https://github.com/graph4ai/graph4nlp) - A library for the easy use of Graph Neural Networks for NLP (DLG4NLP).\n\n* [Jraph](https://github.com/deepmind/jraph) - A Graph Neural Network Library in Jax.\n\n* [cuGraph](https://github.com/rapidsai/cugraph) - A collection of GPU accelerated graph algorithms that process data found in GPU DataFrames (cuDF).\n\n* [GraphEmbedding](https://github.com/shenweichen/GraphEmbedding) - Implementation and experiments of graph embedding algorithms.\n\n* [benchmarking-gnns](https://github.com/graphdeeplearning/benchmarking-gnns) - Repository for benchmarking graph neural networks.\n\n* [PyTorch-BigGraph](https://github.com/facebookresearch/PyTorch-BigGraph) **(not actively updated)** - Generate embeddings from large-scale graph-structured data,  based on PyTorch.\n\n* [TensorFlow Graphics](https://github.com/tensorflow/graphics) **(not actively updated)** - Differentiable Graphics Layers for TensorFlow.\n\n* [StellarGraph](https://github.com/stellargraph/stellargraph) **(not actively updated)** - A Python library for machine learning on graphs and networks.\n\n## Causal Inference\n\n* [EconML](https://github.com/microsoft/EconML) - A Python package for estimating heterogeneous treatment effects from observational data via machine learning.\n\n* [Causal ML](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms.\n\n* [DoWhy](https://github.com/py-why/dowhy) - A Python library for causal inference that supports explicit modeling and testing of causal assumptions.\n\n* [CausalNex](https://github.com/quantumblacklabs/causalnex) - A Python library that helps data scientists to infer causation rather than observing correlation.\n\n* [causallib](https://github.com/IBM/causallib) - A Python package for modular causal inference analysis and model evaluations.\n\n* [pylift](https://github.com/wayfair/pylift) - Uplift modeling package.\n\n* [grf](https://github.com/grf-labs/grf) - Generalized Random Forests.\n\n* [DoubleML](https://github.com/DoubleML/doubleml-for-py) - Double Machine Learning in Python.\n\n* [Causality](https://github.com/akelleh/causality) - Tools for causal analysis.\n\n* [YLearn](https://github.com/DataCanvasIO/YLearn) - A python package for causal inference.\n\n## Recommendation, Advertisement \u0026 Ranking\n\n* [Recommenders](https://github.com/microsoft/recommenders) - Best Practices on Recommendation Systems.\n\n* [Surprise](https://github.com/NicolasHug/Surprise) - A Python scikit for building and analyzing recommender systems.\n\n* [RecLearn](https://github.com/ZiyaoGeng/RecLearn) - Recommender Learning with Tensorflow2.x.\n\n* [Implicit](https://github.com/benfred/implicit) - Fast Python Collaborative Filtering for Implicit Feedback Datasets.\n\n* [LightFM](https://github.com/lyst/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm.\n\n* [RecBole](https://github.com/RUCAIBox/RecBole) - A unified, comprehensive and efficient recommendation library for reproducing and developing recommendation algorithms.\n\n* [DeepCTR](https://github.com/shenweichen/DeepCTR) - Easy-to-use,Modular and Extendible package of deep-learning based CTR models.\n\n* [DeepCTR-Torch](https://github.com/shenweichen/DeepCTR-Torch) - Easy-to-use,Modular and Extendible package of deep-learning based CTR models.\n\n* [deep-ctr-prediction](https://github.com/qiaoguan/deep-ctr-prediction) - CTR prediction models based on deep learning.\n\n* [RecSys](https://github.com/mJackie/RecSys) - 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估。\n\n* [AI-RecommenderSystem](https://github.com/zhongqiangwu960812/AI-RecommenderSystem) - 推荐系统领域的一些经典算法模型。\n\n* [Recommend-System-TF2.0](https://github.com/jc-LeeHub/Recommend-System-tf2.0) - 经典推荐算法的原理解析及代码实现。\n\n* [SparkCTR](https://github.com/wzhe06/SparkCTR) **(not actively updated)** - CTR prediction model based on spark(LR, GBDT, DNN).\n\n* [Awesome-RecSystem-Models](https://github.com/JianzhouZhan/Awesome-RecSystem-Models) **(not actively updated)** - Implements of Awesome RecSystem Models with PyTorch/TF2.0.\n\n* [Deep_Rec](https://github.com/Shicoder/Deep_Rec) **(not actively updated)** - 推荐算法相关代码、文档、资料\n\n## Time-Series \u0026 Financial\n\n* [Prophet](https://github.com/facebook/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.\n\n* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.\n\n* [GluonTS](https://github.com/awslabs/gluonts) - Probabilistic time series modeling in Python.\n\n* [tslearn](https://github.com/tslearn-team/tslearn) - A machine learning toolkit dedicated to time-series data.\n\n* [sktime](https://github.com/sktime/sktime) - A unified framework for machine learning with time series.\n\n* [PyTorch Forecasting](https://github.com/jdb78/pytorch-forecasting) - Time series forecasting with PyTorch.\n\n* [STUMPY](https://github.com/TDAmeritrade/stumpy) - A powerful and scalable Python library for modern time series analysis.\n\n* [StatsForecast](https://github.com/Nixtla/statsforecast) - Offers a collection of widely used univariate time series forecasting models, including automatic ARIMA and ETS modeling optimized for high performance using numba.\n\n* [Orbit](https://github.com/uber/orbit) - A Python package for Bayesian time series forecasting and inference.\n\n* [Pmdarima](https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.\n\n* [Qlib](https://github.com/microsoft/qlib) - An AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.\n\n* [IB-insync](https://github.com/erdewit/ib_insync) - Python sync/async framework for Interactive Brokers API.\n\n* [ffn](https://github.com/pmorissette/ffn) - A financial function library for Python.\n\n* [bt](https://github.com/pmorissette/bt) - A flexible backtesting framework for Python used to test quantitative trading strategies, based on ffn.\n\n* [finmarketpy](https://github.com/cuemacro/finmarketpy) - Python library for backtesting trading strategies \u0026 analyzing financial markets.\n\n* [TensorTrade](https://github.com/tensortrade-org/tensortrade) - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents, based on TensorFlow.\n\n* [TF Quant Finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance.\n\n* [Pandas TA](https://github.com/twopirllc/pandas-ta) - An easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.\n\n* [pyts](https://github.com/johannfaouzi/pyts) **(not actively updated)** - A Python package for time series classification.\n\n* [CryptoSignal](https://github.com/CryptoSignal/Crypto-Signal) **(not actively updated)** - A command line tool that automates your crypto currency Technical Analysis (TA).\n\n* [Catalyst](https://github.com/scrtlabs/catalyst) **(no longer maintained)** - An algorithmic trading library for crypto-assets written in Python.\n\n## Other Machine Learning Applications\n\n* [AlphaFold](https://github.com/deepmind/alphafold) - Open source code for AlphaFold.\n\n* [OpenFold](https://github.com/aqlaboratory/openfold) - Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2.\n\n* [DeepChem](https://github.com/deepchem/deepchem) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.\n\n* [Apollo](https://github.com/ApolloAuto/apollo) - An open autonomous driving platform.\n\n* [OpenCog](https://github.com/opencog/opencog) - A framework for integrated Artificial Intelligence \u0026 Artificial General Intelligence (AGI).\n\n* [Screenshot-to-code](https://github.com/emilwallner/Screenshot-to-code) - A neural network that transforms a design mock-up into a static website.\n\n* [PennyLane](https://github.com/PennyLaneAI/pennylane) - A cross-platform Python library for differentiable programming of quantum computers.\n\n* [OR-Tools](https://github.com/google/or-tools) - Google's Operations Research tools.\n\n* [CARLA](https://github.com/carla-simulator/carla) **(not actively updated)** - An open-source simulator for autonomous driving research.\n\n* [convnet-burden](https://github.com/albanie/convnet-burden) **(not actively updated)** - Memory consumption and FLOP count estimates for convnets.\n\n* [gradient-checkpointing](https://github.com/cybertronai/gradient-checkpointing) **(no longer maintained)** - Make huge neural nets fit in memory.\n\n## Linear Algebra / Statistics Toolkit\n\n### General Purpose Tensor Library\n\n* [NumPy](https://github.com/numpy/numpy) - The fundamental package for scientific computing with Python.\n\n* [SciPy](https://github.com/scipy/scipy) - An open-source software for mathematics, science, and engineering in Python.\n\n* [SymPy](https://github.com/sympy/sympy) - A computer algebra system written in pure Python.\n\n* [ArrayFire](https://github.com/arrayfire/arrayfire) - A general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices\n\n* [CuPy](https://github.com/cupy/cupy) - A NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.\n\n* [PyCUDA](https://github.com/inducer/pycuda) - Pythonic Access to CUDA, with Arrays and Algorithms.\n\n* [Numba](https://github.com/numba/numba) - NumPy aware dynamic Python compiler using LLVM.\n\n* [xtensor](https://github.com/xtensor-stack/xtensor) - C++ tensors with broadcasting and lazy computing.\n\n* [Halide](https://github.com/halide/Halide) - A language for fast, portable data-parallel computation.\n\n* [NumExpr](https://github.com/pydata/numexpr) - Fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, bcolz and more.\n\n* [OpenBLAS](https://github.com/xianyi/OpenBLAS) - An optimized BLAS library based on GotoBLAS2 1.13 BSD version.\n\n* [Bottleneck](https://github.com/pydata/bottleneck) - Fast NumPy array functions written in C.\n\n* [Enoki](https://github.com/mitsuba-renderer/enoki) - Structured vectorization and differentiation on modern processor architectures.\n\n* [Mars](https://github.com/mars-project/mars) - A tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries.\n\n* [TensorLy](https://github.com/tensorly/tensorly) - A Python library that aims at making tensor learning simple and accessible.\n\n* [Pythran](https://github.com/serge-sans-paille/pythran) - An ahead of time compiler for a subset of the Python language, with a focus on scientific computing.\n\n* [Patsy](https://github.com/pydata/patsy) **(no longer maintained)** - Describing statistical models in Python using symbolic formulas.\n\n* [Formulaic](https://github.com/matthewwardrop/formulaic) **(successor of Patsy)** - A high-performance implementation of Wilkinson formulas for Python.\n\n* [Theano](https://github.com/Theano/Theano) **(no longer maintained)** - A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.\n\n* [Aesara](https://github.com/aesara-devs/aesara) **(successor of Theano)** - A Python library that allows one to define, optimize/rewrite, and evaluate mathematical expressions, especially ones involving multi-dimensional arrays.\n\n* [einops](https://github.com/arogozhnikov/einops) - A tensor operation library for NumPy, PyTorch, TensorFlow and JAX.\n\n* [FBGEMM](https://github.com/pytorch/FBGEMM) - A low-precision, high-performance matrix-matrix multiplications and convolution library for server-side inference.\n\n* [taco](https://github.com/tensor-compiler/taco) - A C++ library that computes tensor algebra expressions on sparse and dense tensors.\n\n* [Joblib](https://github.com/joblib/joblib) - Running Python functions as pipeline jobs, with optimizations for numpy.\n\n* [Fastor](https://github.com/romeric/Fastor) - A lightweight high performance tensor algebra framework for modern C++.\n\n* [TiledArray](https://github.com/ValeevGroup/tiledarray) - A massively-parallel, block-sparse tensor framework written in C++.\n\n* [CTF](https://github.com/cyclops-community/ctf) - Cyclops Tensor Framework: parallel arithmetic on multidimensional arrays.\n\n* [Blitz++](https://github.com/blitzpp/blitz) **(not actively updated)** - Multi-Dimensional Array Library for C++.\n\n* [juanjosegarciaripoll/tensor](https://github.com/juanjosegarciaripoll/tensor) **(not actively updated)** - C++ library for numerical arrays and tensor objects and operations with them, designed to allow Matlab-style programming.\n\n* [xtensor-blas](https://github.com/xtensor-stack/xtensor-blas) **(not actively updated)** - BLAS extension to xtensor.\n\n### Tensor Similarity \u0026 Dimension Reduction\n\n* [Milvus](https://github.com/milvus-io/milvus) - An open-source vector database built to power embedding similarity search and AI applications.\n\n* [Faiss](https://github.com/facebookresearch/faiss) - A library for efficient similarity search and clustering of dense vectors.\n\n* [FLANN](https://github.com/flann-lib/flann) - Fast Library for Approximate Nearest Neighbors\n\n* [openTSNE](https://github.com/pavlin-policar/openTSNE) - Extensible, parallel Python implementations of t-SNE.\n\n* [UMAP](https://github.com/lmcinnes/umap) - Uniform Manifold Approximation and Projection, a dimension reduction technique that can be used for visualisation similarly to t-SNE.\n\n### Statistical Toolkit\n\n* [Statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.\n\n* [shap](https://github.com/slundberg/shap) - A game theoretic approach to explain the output of any machine learning model.\n\n* [Pyro](https://github.com/pyro-ppl/pyro) - Deep universal probabilistic programming with Python and PyTorch.\n\n* [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch.\n\n* [PyMC](https://github.com/pymc-devs/pymc) - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara.\n\n* [hmmlearn](https://github.com/hmmlearn/hmmlearn) - Hidden Markov Models in Python, with scikit-learn like API.\n\n* [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant Markov chain Monte Carlo (MCMC).\n\n* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.\n\n* [pomegranate](https://github.com/jmschrei/pomegranate) - Fast, flexible and easy to use probabilistic modelling in Python.\n\n* [Orbit](https://github.com/uber/orbit) - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.\n\n* [GPflow](https://github.com/GPflow/GPflow) - Gaussian processes in TensorFlow.\n\n* [ArviZ](https://github.com/arviz-devs/arviz) - A Python package for exploratory analysis of Bayesian models.\n\n* [POT](https://github.com/PythonOT/POT) - Python Optimal Transport.\n\n* [Edward](https://github.com/blei-lab/edward) **(not actively updated)** - A probabilistic programming language in TensorFlow. Deep generative models, variational inference.\n\n### Others\n\n* [torchdiffeq](https://github.com/rtqichen/torchdiffeq) - Differentiable ordinary differential equation (ODE) solvers with full GPU support and O(1)-memory backpropagation.\n\n* [deal.II](https://github.com/dealii/dealii) - A C++ program library targeted at the computational solution of partial differential equations using adaptive finite elements.\n\n* [Neural ODEs](https://github.com/msurtsukov/neural-ode) - Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations.\n\n* [Quantum](https://github.com/microsoft/Quantum) - Microsoft Quantum Development Kit Samples.\n\n## Data Processing\n\n### Data Representation\n\n* [pandas](https://github.com/pandas-dev/pandas) - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.\n\n* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library.\n\n* [Polars](https://github.com/pola-rs/polars) - Fast multi-threaded DataFrame library in Rust, Python and Node.js.\n\n* [Modin](https://github.com/modin-project/modin) - Scale your Pandas workflows by changing a single line of code.\n\n* [Vaex](https://github.com/vaexio/vaex) - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second.\n\n* [PyTables](https://github.com/PyTables/PyTables) - A Python package to manage extremely large amounts of data.\n\n* [Pandaral.lel](https://github.com/nalepae/pandarallel) - A simple and efficient tool to parallelize Pandas operations on all available CPUs.\n\n* [swifter](https://github.com/jmcarpenter2/swifter) - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner.\n\n* [datatable](https://github.com/h2oai/datatable) - A Python package for manipulating 2-dimensional tabular data structures.\n\n* [xarray](https://github.com/pydata/xarray) - N-D labeled arrays and datasets in Python.\n\n* [Zarr](https://github.com/zarr-developers/zarr-python) - An implementation of chunked, compressed, N-dimensional arrays for Python.\n\n* [Python Sorted Containers](https://github.com/grantjenks/python-sortedcontainers) - Python Sorted Container Types: Sorted List, Sorted Dict, and Sorted Set.\n\n* [Pyrsistent](https://github.com/tobgu/pyrsistent) - Persistent/Immutable/Functional data structures for Python.\n\n* [immutables](https://github.com/MagicStack/immutables) - A high-performance immutable mapping type for Python.\n\n* [DocArray](https://github.com/jina-ai/docarray) - A library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc.\n\n* [Texthero](https://github.com/jbesomi/texthero) - A python toolkit to work with text-based dataset, bases on Pandas.\n\n* [ftfy](https://github.com/rspeer/python-ftfy) - Fixes mojibake and other glitches in Unicode text.\n\n* [Box](https://github.com/cdgriffith/Box) - Python dictionaries with advanced dot notation access.\n\n* [bidict](https://github.com/jab/bidict) - The bidirectional mapping library for Python.\n\n* [anytree](https://github.com/c0fec0de/anytree) - Python tree data library.\n\n* [pydantic](https://github.com/pydantic/pydantic) - Data parsing and validation using Python type hints.\n\n* [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.\n\n### Data Pre-processing \u0026 Loading\n\n* [DALI](https://github.com/NVIDIA/DALI) - A library for data loading and pre-processing to accelerate deep learning applications.\n\n* [Label Studio](https://github.com/heartexlabs/label-studio) - A multi-type data labeling and annotation tool with standardized output format.\n\n* [AugLy](https://github.com/facebookresearch/AugLy) - A data augmentations library for audio, image, text, and video.\n\n* [Albumentations](https://github.com/albumentations-team/albumentations) - A Python library for image augmentation.\n\n* [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library in Python for machine learning.\n\n* [Pillow](https://github.com/python-pillow/Pillow) - The friendly PIL fork (Python Imaging Library).\n\n* [MoviePy](https://github.com/Zulko/moviepy) - Video editing with Python.\n\n* [Open3D](https://github.com/isl-org/Open3D) - A Modern Library for 3D Data Processing.\n\n* [PCL](https://github.com/PointCloudLibrary/pcl) - The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing.\n\n* [imutils](https://github.com/PyImageSearch/imutils) - A basic image processing toolkit in Python, based on OpenCV.\n\n* [Towhee](https://github.com/towhee-io/towhee) - Data processing pipelines for neural networks.\n\n* [ffcv](https://github.com/libffcv/ffcv) - A drop-in data loading system that dramatically increases data throughput in model training.\n\n* [NLPAUG](https://github.com/makcedward/nlpaug) - Data augmentation for NLP.\n\n* [Audiomentations](https://github.com/iver56/audiomentations) - A Python library for audio data augmentation.\n\n* [torch-audiomentations](https://github.com/asteroid-team/torch-audiomentations) - Fast audio data augmentation in PyTorch, with GPU support.\n\n* [librosa](https://github.com/librosa/librosa) - A python package for music and audio analysis.\n\n* [Pydub](https://github.com/jiaaro/pydub) - Manipulate audio with a simple and easy high level interface.\n\n* [DDSP](https://github.com/magenta/ddsp) - A library of differentiable versions of common DSP functions.\n\n* [TSFRESH](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.\n\n* [TA](https://github.com/bukosabino/ta) - A Technical Analysis library useful to do feature engineering from financial time series datasets, based on Pandas and NumPy.\n\n* [Featuretools](https://github.com/alteryx/featuretools) - An open source python library for automated feature engineering.\n\n* [Feature-engine](https://github.com/feature-engine/feature_engine) - A Python library with multiple transformers to engineer and select features for use in machine learning models.\n\n* [img2dataset](https://github.com/rom1504/img2dataset) - Easily turn large sets of image urls to an image dataset.\n\n* [Faker](https://github.com/joke2k/faker) - A Python package that generates fake data for you.\n\n* [SDV](https://github.com/sdv-dev/SDV) - Synthetic Data Generation for tabular, relational and time series data.\n\n* [Googletrans](https://github.com/ssut/py-googletrans) - (unofficial) Googletrans: Free and Unlimited Google translate API for Python. Translates totally free of charge.\n\n* [OptBinning](https://github.com/guillermo-navas-palencia/optbinning) - Monotonic binning with constraints. Support batch \u0026 stream optimal binning. Scorecard modelling and counterfactual explanations.\n\n* [Scrapy](https://github.com/scrapy/scrapy) - A fast high-level web crawling \u0026 scraping framework for Python.\n\n* [pyspider](https://github.com/binux/pyspider) - A Powerful Spider(Web Crawler) System in Python.\n\n* [Instagram Scraper](https://github.com/arc298/instagram-scraper) - Scrapes an instagram user's photos and videos.\n\n* [instaloader](https://github.com/instaloader/instaloader) - Download pictures (or videos) along with their captions and other metadata from Instagram.\n\n* [XueQiuSuperSpider](https://github.com/decaywood/XueQiuSuperSpider) - 雪球股票信息超级爬虫\n\n* [coordtransform](https://github.com/wandergis/coordtransform) - 提供了百度坐标（BD09）、国测局坐标（火星坐标，GCJ02）、和WGS84坐标系之间的转换\n\n* [nlp_chinese_corpus](https://github.com/brightmart/nlp_chinese_corpus) - 大规模中文自然语言处理语料\n\n* [imgaug](https://github.com/aleju/imgaug) **(not actively updated)** - Image augmentation for machine learning experiments.\n\n* [accimage](https://github.com/pytorch/accimage) **(not actively updated)** - High performance image loading and augmenting routines mimicking PIL.Image interface.\n\n* [Snorkel](https://github.com/snorkel-team/snorkel) **(not actively updated)** - A system for quickly generating training data with weak supervision.\n\n* [fancyimpute](https://github.com/iskandr/fancyimpute) **(not actively updated)** - A variety of matrix completion and imputation algorithms implemented in Python.\n\n* [Requests-HTML](https://github.com/psf/requests-html) **(not actively updated)** - Pythonic HTML Parsing for Humans.\n\n* [lazynlp](https://github.com/chiphuyen/lazynlp) **(not actively updated)** - Library to scrape and clean web pages to create massive datasets.\n\n* [Google Images Download](https://github.com/hardikvasa/google-images-download) **(not actively updated)** - Python Script to download hundreds of images from 'Google Images'.\n\n### Data Similarity\n\n* [image-match](https://github.com/ProvenanceLabs/image-match) - a simple package for finding approximate image matches from a corpus.\n\n* [jellyfish](https://github.com/jamesturk/jellyfish) - A library for approximate \u0026 phonetic matching of strings.\n\n* [TextDistance](https://github.com/life4/textdistance) - Python library for comparing distance between two or more sequences by many algorithms.\n\n* [Qdrant](https://github.com/qdrant/qdrant) - A vector similarity search engine for text, image and categorical data in Rust.\n\n### Data Management\n\n* [pandera](https://github.com/unionai-oss/pandera) - A light-weight, flexible, and expressive statistical data testing library.\n\n* [Kedro](https://github.com/kedro-org/kedro) - A Python framework for creating reproducible, maintainable and modular data science code.\n\n* [PyFunctional](https://github.com/EntilZha/PyFunctional) - Python library for creating data pipelines with chain functional programming.\n\n* [ImageHash](https://github.com/JohannesBuchner/imagehash) - An image hashing library written in Python.\n\n* [pandas-profiling](https://github.com/ydataai/pandas-profiling) - Create HTML data profiling reports for pandas DataFrame.\n\n* [FiftyOne](https://github.com/voxel51/fiftyone) - An open-source tool for building high-quality datasets and computer vision models.\n\n* [Datasette](https://github.com/simonw/datasette) - An open source multi-tool for exploring and publishing data.\n\n* [glom](https://github.com/mahmoud/glom) - Python's nested data operator (and CLI), for all your declarative restructuring needs.\n\n* [dedupe](https://github.com/dedupeio/dedupe) - A python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.\n\n* [Ciphey](https://github.com/Ciphey/Ciphey) - Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes.\n\n* [datasketch](https://github.com/ekzhu/datasketch) - Gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy.\n\n## Data Visualization\n\n* [Matplotlib](https://github.com/matplotlib/matplotlib) - A comprehensive library for creating static, animated, and interactive visualizations in Python.\n\n* [Seaborn](https://github.com/mwaskom/seaborn) - A high-level interface for drawing statistical graphics, based on Matplotlib.\n\n* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Data Visualization in the browser, from Python.\n\n* [Plotly.js](https://github.com/plotly/plotly.js) - Open-source JavaScript charting library behind Plotly and Dash.\n\n* [Plotly.py](https://github.com/plotly/plotly.py) - An interactive, open-source, and browser-based graphing library for Python, based on Plotly.js.\n\n* [ggplot2](https://github.com/tidyverse/ggplot2) - An implementation of the Grammar of Graphics in R.\n\n* [ggpy](https://github.com/yhat/ggpy) - ggplot port for python.\n\n* [Datapane](https://github.com/datapane/datapane) - An open-source framework to create data science reports in Python.\n\n* [Visdom](https://github.com/fossasia/visdom) - A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.\n\n* [TabPy](https://github.com/tableau/TabPy) - Execute Python code on the fly and display results in Tableau visualizations.\n\n* [Streamlit](https://github.com/streamlit/streamlit) - The fastest way to build data apps in Python.\n\n* [HyperTools](https://github.com/ContextLab/hypertools) - A Python toolbox for gaining geometric insights into high-dimensional data, based on Matplotlib and Seaborn.\n\n* [Dash](https://github.com/plotly/dash) - Analytical Web Apps for Python, R, Julia and Jupyter, based on Plotly.js.\n\n* [mpld3](https://github.com/mpld3/mpld3) - An interactive Matplotlib visualization tool in browser, based on D3.\n\n* [Vega](https://github.com/vega/vega) - A visualization grammar, a declarative format for creating, saving, and sharing interactive visualization designs.\n\n* [Vega-Lite](https://github.com/vega/vega-lite) - Provides a higher-level grammar for visual analysis that generates complete Vega specifications.\n\n* [Vega-Altair](https://github.com/altair-viz/altair) - A declarative statistical visualization library for Python, based on Vega-Lite.\n\n* [PyQtGraph](https://github.com/pyqtgraph/pyqtgraph) - Fast data visualization and GUI tools for scientific / engineering applications.\n\n* [VisPy](https://github.com/vispy/vispy) - A high-performance interactive 2D/3D data visualization library, with OpenGL support.\n\n* [PyVista](https://github.com/pyvista/pyvista) - 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK).\n\n* [Potree](https://github.com/potree/potree) - WebGL point cloud viewer for large datasets.\n\n* [Holoviews](https://github.com/holoviz/holoviews) - An open-source Python library designed to make data analysis and visualization seamless and simple.\n\n* [Graphviz](https://github.com/xflr6/graphviz) - Python interface for Graphviz to create and render graphs.\n\n* [PyGraphistry](https://github.com/graphistry/pygraphistry) - A Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer.\n\n* [Apache ECharts](https://github.com/apache/echarts) - A powerful, interactive charting and data visualization library for browser.\n\n* [pyecharts](https://github.com/pyecharts/pyecharts) - A Python visualization interface for Apache ECharts.\n\n* [word_cloud](https://github.com/amueller/word_cloud) - A little word cloud generator in Python.\n\n* [Datashader](https://github.com/holoviz/datashader) - A data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data.\n\n* [Perspective](https://github.com/finos/perspective) - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.\n\n* [ggplot2](https://github.com/tidyverse/ggplot2) - An implementation of the Grammar of Graphics in R.\n\n* [plotnine](https://github.com/has2k1/plotnine) - An implementation of the Grammar of Graphics in Python, based on ggplot2.\n\n* [bqplot](https://github.com/bqplot/bqplot) - An implementation of the Grammar of Graphics for IPython/Jupyter notebooks.\n\n* [D-Tale](https://github.com/man-group/dtale) - A visualization tool for Pandas DataFrame, with ipython notebooks support.\n\n* [missingno](https://github.com/ResidentMario/missingno) - A Python visualization tool for missing data.\n\n* [HiPlot](https://github.com/facebookresearch/hiplot) - A lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data.\n\n* [Sweetviz](https://github.com/fbdesignpro/sweetviz) - Visualize and compare datasets, target values and associations, with one line of code.\n\n* [Netron](https://github.com/lutzroeder/netron) - Visualizer for neural network, deep learning, and machine learning models.\n\n* [livelossplot](https://github.com/stared/livelossplot) - Live training loss plot in Jupyter Notebook for Keras, PyTorch and others.\n\n* [Diagrams](https://github.com/mingrammer/diagrams) - Lets you draw the cloud system architecture in Python code.\n\n* [SandDance](https://github.com/microsoft/SandDance) - Visually explore, understand, and present your data.\n\n* [ML Visuals](https://github.com/dair-ai/ml-visuals) - Contains figures and templates which you can reuse and customize to improve your scientific writing.\n\n* [Scattertext](https://github.com/JasonKessler/scattertext) **(not actively updated)** - A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot.\n\n* [TensorSpace.js](https://github.com/tensorspace-team/tensorspace) - Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js.\n\n* [Netscope](https://github.com/ethereon/netscope) **(not actively updated)** - Neural network visualizer.\n\n* [draw_convnet](https://github.com/gwding/draw_convnet) **(not actively updated)** - Python script for illustrating Convolutional Neural Network (ConvNet).\n\n* [PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet) **(not actively updated)** - Latex code for making neural networks diagrams.\n\n## Machine Learning Tutorials\n\n* [PyTorch official tutorials](https://pytorch.org/tutorials/) - Official tutorials for PyTorch.\n\n* [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) - State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.\n\n* [Learn OpenCV](https://github.com/spmallick/learnopencv) - C++ and Python Examples.\n\n* [nlp-with-transformers](https://github.com/nlp-with-transformers/notebooks) - Jupyter notebooks for the Natural Language Processing with Transformers book.\n\n* [labml.ai](https://nn.labml.ai/) - A collection of PyTorch implementations of neural networks and related algorithms, which are documented with explanations and rendered as side-by-side formatted notes.\n\n* [Machine Learning Notebooks](https://github.com/ageron/handson-ml) **(no longer maintained)** - This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in my O'Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow.\n\n* [Machine Learning Notebooks, 3rd edition](https://github.com/ageron/handson-ml3) **(successor of Machine Learning Notebooks)** - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.\n\n* [Made With ML](https://github.com/GokuMohandas/Made-With-ML) - Learn how to responsibly develop, deploy and maintain production machine learning applications.\n\n* [Reinforcement-learning-with-tensorflow](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow) - Simple Reinforcement learning tutorials.\n\n* [Jezzamonn/fourier](https://github.com/Jezzamonn/fourier) - An Interactive Introduction to Fourier Transforms.\n\n* [adv-financial-ml-marcos-exercises](https://github.com/fernandodelacalle/adv-financial-ml-marcos-exercises) - Exercises of the book: Advances in Financial Machine Learning by Marcos Lopez de Prado.\n\n* [d2l-zh](https://github.com/d2l-ai/d2l-zh) - 《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60个国家的400所大学用于教学。\n\n* [nndl.github.io](https://github.com/nndl/nndl.github.io) - 《神经网络与深度学习》 邱锡鹏著\n\n* [AI-Job-Notes](https://github.com/amusi/AI-Job-Notes) - AI算法岗求职攻略（涵盖准备攻略、刷题指南、内推和AI公司清单等资料）\n\n* [Te","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/johnhany%2Fawesome-list/projects"}