Search awesome repositories

Bayesian optimization in PyTorch

Models, data loaders and abstractions for language processing, powered by PyTorch

Fast, flexible and easy to use probabilistic modelling in Python.

A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities

Generate embeddings from large-scale graph-structured data.

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

Datetimes for Humans™

Accelerated deep learning R&D

Python Toolkit for Causal and Probabilistic Reasoning

Tensorforce: a TensorFlow library for applied reinforcement learning

NumPy and Pandas interface to Big Data

The machine learning toolkit for time series analysis in Python

TensorFlow Reinforcement Learning

Visualize and compare datasets, target values and associations, with one line of code.

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

High performance datastore for time series and tick data

Shōgun

StellarGraph - Machine Learning on Graphs

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

Data manipulation and transformation for audio signal processing, powered by PyTorch

With Holoviews, your data visualizes itself.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Pandas integration with sklearn

POT : Python Optimal Transport

Sequential model-based optimization with a `scipy.optimize` interface

Adaptive Experimentation Platform

A library for debugging/inspecting machine learning classifiers and explaining their predictions