GitHub projects

Bolt Online Learning Toolbox. [Deprecated]

Restricted Boltzmann Machines in Python. [DEEP LEARNING]

Implementation of image to image (pix2pix) translation from the paper by isola et al.[DEEP LEARNING]

Book on Bayesian Analysis.

A Python library for implementing a Recommender System.

A flexible, fast recommender engine. [Deprecated]

Fast, flexible and fun neural networks. This is the successor of PyBrain.

Another Python Machine Learning Library.

Topic modelling toolkit. [Deprecated]

Interactive topic model visualization/interpretation framework.

Topic Modelling for Humans.

A scikit-learn native toolkit for nonprofit and academic-medical-center fundraising analytics: leakage-safe donor propensity, lapse, planned-giving, wealth-screening and revenue-forecasting estimators.

Open source library with an exhaustive battery of feature engineering and selection methods based on pandas and scikit-learn.

Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

Flexible neural network framework.

GPU-Accelerated Deep Learning Library in Python. [Deprecated]

Lightweight library to build and train neural networks in Theano.

High-level neural networks frontend for TensorFlow, CNTK and Theano.

A Machine Learning library based on Theano. [Deprecated]

Numenta Platform for Intelligent Computing.

Enterprise-grade LLM integration framework for building production-ready AI applications with built-in hallucination prevention, RAG, and MCP support.

Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.

A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.

A PyTorch-based framework to train and validate the models producing high-quality embeddings.

Universal memory service with semantic search, autonomous consolidation, and multi-client support for AI applications.

A Python module for metric learning.

A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.

Book/iPython notebooks on Probabilistic Programming in Python.

a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some advanced bagging and boosting techniques such as gradient boosting, random forest and adaboost.

Unified API for SHAP, LIME, permutation importance, and partial dependence explanations.

InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations.

Deep learning operations reinvented (for pytorch, tensorflow, jax and others).

High performance library for time series distances (DTW) and time series clustering.

Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.

> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.

> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.

> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.

> A graph sampling extension library for NetworkX with a Scikit-Learn like API.

> A temporal extension of PyTorch Geometric for dynamic graph representation learning.

> Graph Neural Network Library for PyTorch.

PyTorch Frame -> A Modular Framework for Multi-Modal Tabular Learning.

> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.

> A delightful machine learning tool that allows you to train/fit, test and use models without writing code

> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.

> A distributed machine learning framework Apache Spark

> A PyTorch based deep learning library for drug pair scoring

> A general purpose recommender metrics library for fair evaluation.