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An open API service indexing awesome lists of open source software.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.
https://github.com/eric-erki/awesome-machine-learning

Last synced: 24 days ago
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C++

Speech Recognition

  • CatBoost - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
  • Shogun - The Shogun Machine Learning Toolbox.
  • LKYDeepNN - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
  • xLearn - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.

Java

Speech Recognition

  • Datumbox - Machine Learning framework for rapid development of Machine Learning and Statistical applications.
  • H2O - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
  • SmileMiner - Statistical Machine Intelligence & Learning Engine.

Javascript

Speech Recognition

  • machineJS - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!

Julia

Speech Recognition

  • GLM - Generalized linear models in Julia.
  • Knet - Koç University Deep Learning Framework.
  • Distributions - A Julia package for probability distributions and associated functions.

PHP

General-Purpose Machine Learning

  • 19 Questions - A machine learning / bayesian inference assigning attributes to objects.

Python

General-Purpose Machine Learning

  • auto_ml - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
  • Bayesian Methods for Hackers - Book/iPython notebooks on Probabilistic Programming in Python.
  • keras - High-level neural networks frontend for TensorFlow, CNTK and Theano.
  • mlxtend - A library consisting of useful tools for data science and machine learning tasks.
  • TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
  • Xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
  • ML-From-Scratch - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
  • statsmodels - Statistical modeling and econometrics in Python.
  • scikit-plot - A visualization library for quick and easy generation of common plots in data analysis and machine learning.
  • Bowtie - A dashboard library for interactive visualizations using flask socketio and react.
  • PyCM - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters
  • Dash - A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
  • data-science-ipython-notebooks - Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
  • Introduction to machine learning with scikit-learn - IPython notebooks from Data School's video tutorials on scikit-learn.

Scala

General-Purpose Machine Learning

  • doddle-model - An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.