Machine-Learning Python General-Purpose Machine Learning
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Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found [here](https://docs.microsoft.com/cognitive-toolkit/).Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.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.automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.Python bindings for eXtreme Gradient Boosting (Tree) Library.An Apache Incubating project for developing an open source machine learning library.Book/iPython notebooks on Probabilistic Programming in Python.Distributed machine learning library in Sparka service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.A Python module for machine learning built on top of SciPy.A Python module for metric learning.A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.Machine Learning and Data Mining for Astronomy.A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.A library that contacts external servers.Web mining module for Python.Numenta Platform for Intelligent Computing.High-level neural networks frontend for [TensorFlow](https://github.com/tensorflow/tensorflow), [CNTK](https://github.com/Microsoft/CNTK) and [Theano](https://github.com/Theano/Theano).Lightweight library to build and train neural networks in Theano.Flexible neural network framework.Fast and automated time series forecasting framework by Facebook.Topic Modelling for Humans.Another Python Machine Learning Library.Fast, flexible and fun neural networks. This is the successor of PyBrain.A scikit for building and analyzing recommender systems.Fast Python Collaborative Filtering for Implicit Datasets. A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.A Python library for implementing a Recommender System.Book on Bayesian Analysis.Implementation of image to image (pix2pix) translation from the paper by [isola et al](https://arxiv.org/pdf/1611.07004.pdf).[DEEP LEARNING]Machine learning for NeuroImaging in Python.Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful.Python module to perform under sampling and oversampling with various techniques.Python toolbox for quick implementation, modification, evaluation, and visualization of ensemble learning algorithms for class-imbalanced data. Supports out-of-the-box multi-class imbalanced (long-tailed) classification.The Shogun Machine Learning Toolbox.A deep learning framework developed with cleanliness, readability, and speed in mind.Theano based library for deep and recurrent neural networks.Open source platform for deploying machine learning models in production.library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.A wrapper around scikit-learn that makes it simpler to conduct experiments.Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python.Open source software library for numerical computation using data flow graphs.Hidden Markov Models for Python, implemented in Cython for speed and efficiency.A Python extension module wrapping the full TiMBL C++ programming interface. Timbl is an elaborate k-Nearest Neighbours machine learning toolkit.Evolutionary algorithm framework.A library consisting of useful tools for data science and machine learning tasks.A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search.Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].Approximate nearest neighbours implementation.Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.Open source data visualization and data analysis for novices and experts.Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.Deep learning library featuring a higher-level API for TensorFlow.Python bindings for Regularized Greedy Forest (Tree) Library.Python package for Bayesian Machine Learning with scikit-learn API.Simple machine learning library, including Perceptron, Regression, Support Vector Machine, Decision Tree and more, it's easy to use and easy to learn for beginners.A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.Tensors and Dynamic neural networks in Python with strong GPU accelerationThe lightweight PyTorch wrapper for high-performance AI research.Toolbox of models, callbacks, and datasets for AI/ML researchers.A scikit-learn compatible neural network library that wraps PyTorch.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.A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even multi-GPU) computation.Implementation of machine learning stacking technique as a handy library in Python.A modular active learning framework for Python, built on top of scikit-learn.Parris, the automated infrastructure setup tool for machine learning algorithms.neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.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.A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.Visualizer for machine learning models.Machine Learning Prediction System on AWS LambdaOpen Source framework to streamline use of neural networks.A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.JAX is Autograd and XLA, brought together for high-performance machine learning research.High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.A machine learning framework for multi-output/multi-label and stream data.A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.A simple, but essential Bayesian optimization package, written in Python.An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.A fast Evolution Strategy implementation in Python.Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.A Python library for secure and private Deep Learning built on PyTorch and TensorFlow.Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyftA unified framework for machine learning with time seriesA Python-inspired implementation of the Optimum-Path Forest classifier.Python-based meta-heuristic optimization techniques.A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit [activeloop.ai](https://activeloop.ai) for more info.Multidimensional synthetic data generation in Python.An easy-to-use, Python-based feature store. Optimized for time-series data.Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.