ML Libraries

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Web

  • ​ml5.js - Friendly machine learning for the web.

Other

  • ​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.

  • ​numpy-ml - Machine learning, in numpy.

  • ​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.

  • ​MLflow - Machine Learning Lifecycle Platform.

  • ​auto-sklearn - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

  • ​TensorNetwork - Library for easy and efficient manipulation of tensor networks.

  • ​lambda-ml - Small machine learning library aimed at providing simple, concise implementations of machine learning techniques and utilities.

  • ​scikit-learn - Python module for machine learning built on top of SciPy. (Tutorials)

  • ​MLBox - Powerful Automated Machine Learning python library.

  • ​Mlxtend (machine learning extensions) - Python library of useful tools for the day-to-day data science tasks.

  • ​CrypTen - Framework for Privacy Preserving Machine Learning built on PyTorch.

  • ​Faiss - Library for efficient similarity search and clustering of dense vectors.

  • ​pyHSICLasso - Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data.

  • ​AutoGluon - AutoML Toolkit for Deep Learning.

  • ​DeepLearning.scala - Simple library for creating complex neural networks from object-oriented and functional programming constructs.

  • ​Optuna - Hyperparameter optimization framework.

  • ​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.

  • ​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.

  • ​FATE - Industrial Level Federated Learning Framework.

  • ​Deepkit - Collaborative and real-time machine learning training suite: Experiment execution, tracking, and debugging.

  • ​Sls - Stochastic Line Search.

  • ​PyCaret - Open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment.

  • ​Flax - Neural network library for JAX designed for flexibility.

  • ​scikit-multilearn - Python module capable of performing multi-label learning tasks.

  • ​imbalanced-learn - Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

  • ​DeepSpeed - Deep learning optimization library that makes distributed training easy, efficient, and effective.

  • ​HoMM - Library for Homoiconic Meta-mapping.

  • ​Hummingbird - Library for compiling trained traditional ML models into tensor computations.

  • ​Ax - Accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.

  • ​Neuropod - Uniform interface to run deep learning models from multiple frameworks.

  • ​aerosolve - Machine learning package built for humans in Scala.

  • ​Kur - Descriptive Deep Learning.

  • ​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.