5358 Repositories
Python swift-machine-learning Libraries
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this
Lightweight library to build and train neural networks in Theano
Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
NuPIC Numenta Platform for Intelligent Computing The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implem
A toolkit for making real world machine learning and data analysis applications in C++
dlib C++ library Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real worl
Machine learning framework for both deep learning and traditional algorithms
NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. This framework is used by ABBYY engineers for
ThunderGBM: Fast GBDTs and Random Forests on GPUs
Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o
torchbearer: A model fitting library for PyTorch
Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll
JAX-based neural network library
Haiku: Sonnet for JAX Overview | Why Haiku? | Quickstart | Installation | Examples | User manual | Documentation | Citing Haiku What is Haiku? Haiku i
ThunderSVM: A Fast SVM Library on GPUs and CPUs
What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
Documentation | FAQ | Release Notes | Roadmap | MACE Model Zoo | Demo | Join Us | 中文 Mobile AI Compute Engine (or MACE for short) is a deep learning i
fklearn: Functional Machine Learning
fklearn: Functional Machine Learning fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning. Th
Shōgun
The SHOGUN machine learning toolbox Unified and efficient Machine Learning since 1999. Latest release: Cite Shogun: Develop branch build status: Donat
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
Overview | Tutorials | Examples | Installation | FAQ | How to Cite Welcome to ktrain News and Announcements 2020-11-08: ktrain v0.25.x is released and
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)
This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.
Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on
mlpack: a scalable C++ machine learning library --
a fast, flexible machine learning library Home | Documentation | Doxygen | Community | Help | IRC Chat Download: current stable version (3.4.2) mlpack
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Click on the image to
A scikit-learn compatible neural network library that wraps PyTorch
A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes
TensorFlow-based neural network library
Sonnet Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learn
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built
Turi Create simplifies the development of custom machine learning models.
Quick Links: Installation | Documentation | WWDC 2019 | WWDC 2018 Turi Create Check out our talks at WWDC 2019 and at WWDC 2018! Turi Create simplifie
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin
Deep learning library featuring a higher-level API for TensorFlow.
TFLearn: Deep learning library featuring a higher-level API for TensorFlow. TFlearn is a modular and transparent deep learning library built on top of
A flexible framework of neural networks for deep learning
Chainer: A deep learning framework Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX Forum (en, ja
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries From the makers of spaCy, Prodigy and FastAPI Thinc is a
The fastai deep learning library
Welcome to fastai fastai simplifies training fast and accurate neural nets using modern best practices Important: This documentation covers fastai v2,
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l
Deep Learning for humans
Keras: Deep Learning for Python Under Construction In the near future, this repository will be used once again for developing the Keras codebase. For
Tensors and Dynamic neural networks in Python with strong GPU acceleration
PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b
scikit-learn: machine learning in Python
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started
An Open Source Machine Learning Framework for Everyone
Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a
A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.
PyArmor Homepage (中文版网站) Documentation(中文版) PyArmor is a command line tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine
Lazydata: Scalable data dependencies for Python projects
lazydata: scalable data dependencies lazydata is a minimalist library for including data dependencies into Python projects. Problem: Keeping all data
Automatic caching and invalidation for Django models through the ORM.
Cache Machine Cache Machine provides automatic caching and invalidation for Django models through the ORM. For full docs, see https://cache-machine.re
Django friendly finite state machine support
Django friendly finite state machine support django-fsm adds simple declarative state management for django models. If you need parallel task executio
Display machine state using Python3 with Flask.
Flask-State English | 简体中文 Flask-State is a lightweight chart plugin for displaying machine state data in your web application. Monitored Metric: CPU,
A Smart, Automatic, Fast and Lightweight Web Scraper for Python
AutoScraper: A Smart, Automatic, Fast and Lightweight Web Scraper for Python This project is made for automatic web scraping to make scraping easy. It
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Best-of Machine Learning with Python 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. This curated list contains 840 awe
Implementation of Feedback Transformer in Pytorch
Feedback Transformer - Pytorch Simple implementation of Feedback Transformer in Pytorch. They improve on Transformer-XL by having each token have acce
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package
trRosetta - Pytorch (wip) Implementation of trRosetta and trDesign for Pytorch, made into a convenient package
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning This is the PyTorch implementation of our paper: FeatMatch: Feature-Based Augmentat
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
DALL-E in Pytorch Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the ge
Deploy a ML inference service on a budget in less than 10 lines of code.
BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.
PORORO: Platform Of neuRal mOdels for natuRal language prOcessing
PORORO: Platform Of neuRal mOdels for natuRal language prOcessing pororo performs Natural Language Processing and Speech-related tasks. It is easy to
Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning.
Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning Installation
Learning Continuous Image Representation with Local Implicit Image Function
LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo
Implementation of Bottleneck Transformer in Pytorch
Bottleneck Transformer - Pytorch Implementation of Bottleneck Transformer, SotA visual recognition model with convolution + attention that outperforms
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
Trankit: A Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing Trankit is a light-weight Transformer-based Pyth
机器学习、深度学习、自然语言处理等人工智能基础知识总结。
说明 机器学习、深度学习、自然语言处理基础知识总结。 目前主要参考李航老师的《统计学习方法》一书,也有一些内容例如XGBoost、聚类、深度学习相关内容、NLP相关内容等是书中未提及的。
Easy to use, state-of-the-art Neural Machine Translation for 100+ languages
EasyNMT - Easy to use, state-of-the-art Neural Machine Translation This package provides easy to use, state-of-the-art machine translation for more th
⚾🤖⚾ Automatic baseball pitching overlay in realtime
⚾ Automatically overlaying pitch motion and trajectory with machine learning! This project takes your baseball pitching clips and automatically genera
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)
Deep Daze mist over green hills shattered plates on the grass cosmic love and attention a time traveler in the crowd life during the plague meditative
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN
artificial intelligence cosmic love and attention fire in the sky a pyramid made of ice a lonely house in the woods marriage in the mountains lantern
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin
🛠️ Learn a technology X by doing a project - Search engine of project-based learning
Learn X by doing Y 🛠️ Learn a technology X by doing a project Y Website You can contribute by adding projects to the CSV file.
Implementation of the Point Transformer layer, in Pytorch
Point Transformer - Pytorch Implementation of the Point Transformer self-attention layer, in Pytorch. The simple circuit above seemed to have allowed
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.
Graph Transformer Architecture. Source code for
Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" by Vijay Prakash Dwivedi and Xavier Bres
RLStructures is a library to facilitate the implementation of new reinforcement learning algorithms.
RLStructures is a lightweight Python library that provides simple APIs as well as data structures that make as few assumptions as possibl
Script utilizando OpenCV e modelo Machine Learning para detectar o uso de máscaras.
Reconhecendo máscaras Este repositório contém um script em Python3 que reconhece se um rosto está ou não portando uma máscara! O código utiliza da bib
Big Bird: Transformers for Longer Sequences
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early
AWS DeepRacer Free Student Workshop: Run faster by using your custom waypoints
AWS DeepRacer Free Student Workshop: Run faster by using your custom waypoints Reward Function Template for waypoints def reward_function(params):
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.
UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive
Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020)
Learning to Simulate Dynamic Environments with GameGAN PyTorch code for GameGAN Learning to Simulate Dynamic Environments with GameGAN Seung Wook Kim,
Explainability for Vision Transformers (in PyTorch)
Explainability for Vision Transformers (in PyTorch) This repository implements methods for explainability in Vision Transformers
Code for the paper Learning the Predictability of the Future
Learning the Predictability of the Future Code from the paper Learning the Predictability of the Future. Website of the project in hyperfuture.cs.colu
State of the Art Neural Networks for Deep Learning
pyradox This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2
PhoNLP: A BERT-based multi-task learning toolkit for part-of-speech tagging, named entity recognition and dependency parsing
PhoNLP is a multi-task learning model for joint part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently.
Official TensorFlow code for the forthcoming paper
~ Efficient-CapsNet ~ Are you tired of over inflated and overused convolutional neural networks? You're right! It's time for CAPSULES :)
Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch.
SE3 Transformer - Pytorch Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. May be needed for replicating Alphafold2 resu
Performant, differentiable reinforcement learning
deluca Performant, differentiable reinforcement learning Notes This is pre-alpha software and is undergoing a number of core changes. Updates to follo
An open framework for Federated Learning.
Welcome to Intel® Open Federated Learning Federated learning is a distributed machine learning approach that enables organizations to collaborate on m
Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch
Lie Transformer - Pytorch (wip) Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch. Only the SE3 version will be present in thi
Implementation of Supervised Contrastive Learning with AMP, EMA, SWA, and many other tricks
SupCon-Framework The repo is an implementation of Supervised Contrastive Learning. It's based on another implementation, but with several differencies
Sample code from the Neural Networks from Scratch book.
Neural Networks from Scratch (NNFS) book code Code from the NNFS book (https://nnfs.io) separated by chapter.
Code for "Layered Neural Rendering for Retiming People in Video."
Layered Neural Rendering in PyTorch This repository contains training code for the examples in the SIGGRAPH Asia 2020 paper "Layered Neural Rendering
Age and Gender prediction using Keras
cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.
Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un
Client library to download and publish models and other files on the huggingface.co hub
huggingface_hub Client library to download and publish models and other files on the huggingface.co hub Do you have an open source ML library? We're l
[AAAI 21] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
◥ Curriculum Labeling ◣ Revisiting Pseudo-Labeling for Semi-Supervised Learning Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez. In the
This repository contains code examples and documentation for learning how applications can be developed with Kubernetes
BigBitBus KAT Components Click on the diagram to enlarge, or follow this link for detailed documentation Introduction Welcome to the BigBitBus Kuberne
A PyTorch re-implementation of the paper 'Exploring Simple Siamese Representation Learning'. Reproduced the 67.8% Top1 Acc on ImageNet.
Exploring simple siamese representation learning This is a PyTorch re-implementation of the SimSiam paper on ImageNet dataset. The results match that
FLEX (Federated Learning EXchange,FLEX) protocol is a set of standardized federal learning agreements designed by Tongdun AI Research Group。
Click to view Chinese version FLEX (Federated Learning Exchange) protocol is a set of standardized federal learning agreements designed by Tongdun AI
Transformers are Graph Neural Networks!
🚀 Gated Graph Transformers Gated Graph Transformers for graph-level property prediction, i.e. graph classification and regression. Associated article
The fastest way to visualize GradCAM with your Keras models.
VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)
R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
CyGNet This repository reproduces the AAAI'21 paper “Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Network
FedJAX is a library for developing custom Federated Learning (FL) algorithms in JAX.
FedJAX: Federated learning with JAX What is FedJAX? FedJAX is a library for developing custom Federated Learning (FL) algorithms in JAX. FedJAX priori
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)
Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the
Spectrum is an AI that uses machine learning to generate Rap song lyrics
Spectrum Spectrum is an AI that uses deep learning to generate rap song lyrics. View Demo Report Bug Request Feature Open In Colab About The Project S
Graph neural network message passing reframed as a Transformer with local attention
Adjacent Attention Network An implementation of a simple transformer that is equivalent to graph neural network where the message passing is done with