imgaug - Image augmentation for machine learning experiments.
PlaidML - Framework for making deep learning work everywhere.
Leaf - Open Machine Intelligence Framework for Hackers. (GPU/CPU).
Apache MXNet - Deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity.
Sonnet - Library built on top of TensorFlow for building complex neural networks.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators.
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
PySyft - Library for encrypted, privacy preserving deep learning.
cuML - Suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
ONNX Runtime - Cross-platform, high performance scoring engine for ML models.
Vowpal Wabbit - Machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. (Web) (Article)
Brancher - User-centered Python package for differentiable probabilistic inference.
Karate Club - General purpose community detection and network embedding library for research built on NetworkX.
FlexFlow - Distributed deep learning framework that supports flexible parallelization strategies.
DeltaPy - Tabular Data Augmentation & Feature Engineering.
TensorStore - Library for reading and writing large multi-dimensional arrays.
NNI (Neural Network Intelligence) - Lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.
LMfit-py - Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize.leastsq, and with many additional classes and methods for curve fitting.
tslearn - Machine learning toolkit for time series analysis in Python.
Libra - Ergonomic machine learning for everyone. (Docs)
NGBoost - Natural Gradient Boosting for Probabilistic Prediction.
LightGBM - Gradient boosting framework that uses tree based learning algorithms.
XGBoost - Optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.
DMLC-Core - Common bricks library for building scalable and portable distributed machine learning.
Linear Models - Add linear models including instrumental variable and panel data models that are missing from statsmodels.
skift - scikit-learn wrappers for Python fastText.
pulearn - Positive-unlabeled learning with Python.
pescador - Library for streaming (numerical) data, primarily for use in machine learning applications.
GraKeL - Library that provides implementations of several well-established graph kernels. scikit-learn compatible.
creme - Python library for online machine learning. All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data. (Docs)
RecBole - Unified, comprehensive and efficient recommendation library.
NNFusion - Flexible and efficient DNN compiler that can generate high-performance executables from a DNN model description.
ncnn - High-performance neural network inference computing framework optimized for mobile platforms.
Scikit-Optimize - Sequential model-based optimization with a scipy.optimize interface.
scikit-rebate - Scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
Fedlearner - Collaborative machine learning frameowork that enables joint modeling of data distributed between institutions.
SkLearn2PMML - Python library for converting Scikit-Learn pipelines to PMML.
vecstack - Python package for stacking (machine learning technique).
LightSeq - High Performance Inference Library for Sequence Processing and Generation.
modestpy - Facilitates parameter estimation in models compliant with Functional Mock-up Interface.
Distiller - Open-source Python package for neural network compression research.
modAL - Modular active learning framework for Python.
Bambi - BAyesian Model-Building Interface in Python.
Bolt - Deep learning library with high performance and heterogeneous flexibility.
hypothesis - Python toolkit for (simulation-based) inference and the mechanization of science.
brain.js - GPU accelerated Neural networks in JavaScript for Browsers and Node.js. (Web)
Buffalo - Fast and scalable production-ready open source project for recommender systems.
EvalML - AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
MindSpore - New open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Flashlight - Fast, Flexible Machine Learning in C++.
raster-deep-learning - ArcGIS built-in python raster functions for deep learning to get you started fast.
CTranslate2 - Fast inference engine for OpenNMT models.
Causal Discovery Toolbox - Algorithms for graph structure recovery (including algorithms from the bnlearn, pcalg packages), mainly based out of observational data.
FedML - Research Library and Benchmark for Federated Machine Learning.
Auto_TS - Automatically build multiple Time Series models using a Single Line of Code.
cleanlab - Machine learning python package for learning with noisy labels and finding label errors in datasets. (Web) (Lobsters)
deeptime - Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation.
Jelly Bean World - Framework for experimenting with never-ending learning.
Larq - Open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs). (Web)
tsai - State-of-the-art Deep Learning for Time Series and Sequence Modeling.
edbo - Experimental Design via Bayesian Optimization.
TensorJS - JS/TS library for accelerated tensor computation intended to be run in the browser.
micro-TCN - Efficient neural networks for audio effect modeling. (Web)
DESlib - Python library for dynamic classifier and ensemble selection.
BytePS - High performance and generic framework for distributed DNN training.
Hyperactive - Hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-learning models.
Jittor - Just-in-time(JIT) deep learning framework.
autofeat - Linear Prediction Model with Automated Feature Engineering and Selection Capabilities.
Distrax - Lightweight library of probability distributions and bijectors. It acts as a JAX-native reimplementation of a subset of TensorFlow Probability (TFP).
RevLib - Simple and efficient RevNet-Library with DeepSpeed support.
DeepSparse - Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs.
NVTabular - Engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning (DL) based recommender systems.
Treeo - Small library for creating and manipulating custom JAX Pytree classes.
FedJAX - JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.
oneAPI - OneAPI Deep Neural Network Library (oneDNN).
MosaicML Composer - Library of methods, and ways to compose them together for more efficient ML training.
deep-significance - Easy and Better Significance Testing for Deep Neural Networks.
Finetuner - Finetuning any DNN for better embedding on neural search tasks. (Docs)
mlcrate - Hon module of handy tools and functions, mainly for ML and Kaggle.
OSLO - Open Source framework for Large-scale transformer Optimization.
snntorch - Deep and online learning with spiking neural networks in Python.
NVIDIA DALI - GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
MIPLearn - Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML).
tree-math - Mathematical operations for JAX pytrees.
ExplainX - Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
Contextual AI - Adds explainability to different stages of machine learning pipelines.
NeuralForecast - Python library for time series forecasting with deep learning models.
pythae - Library for Variational Autoencoder benchmarking.
Pyraug - Data Augmentation with Variational Autoencoders.
product-quantization - Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search.
OTT - Sturdy, versatile and efficient optimal transport solvers, taking advantage of JAX features, such as JIT, auto-vectorization and implicit differentiation.
Marian - Efficient Neural Machine Translation framework written in pure C++ with minimal dependencies. (Web)
segmind - MLOps for end-to-end deep learning lifecycle.
FLUTE - Federated Learning Utilities and Tools for Experimentation.
HoloClean - Machine Learning System for Data Enrichment. Built on top of PyTorch and PostgreSQL.
OpenDelta - Open-Source Framework for Paramter Efficient Tuning (Delta Tuning).
Alpa - Automatically parallelizes tensor computational graphs and runs them on a distributed cluster.
GPBoost - Combining Tree-Boosting with Gaussian Process and Mixed Effects Models.
CORDS - Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.
DISTIL - Cut down your labeling cost and time by 3x-5x.
OpenFL - Open-Source Framework For Federated Learning.
Basenji - Sequential regulatory activity predictions with deep convolutional neural networks.
mmap.ninja - Library for storing your datasets in memory-mapped files, which leads to a dramatic speedup in the training time. Accelerate the iteration over your machine learning dataset by up to 20 times.
geomloss - Geometric loss functions between point clouds, images and volumes.
morphsnakes - Implementation of the Morphological Snakes for image segmentation. Supports 2D images and 3D volumes.
HyperLib - Common Neural Network components in the hyperbolic space (using the Poincare model).