Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Machine learning, in numpy

A Python implementation of global optimization with gaussian processes.

A highly efficient implementation of Gaussian Processes in PyTorch

Gaussian processes in TensorFlow

Kriging Toolkit for Python

Gaussian processes in JAX.

Bayesian Reinforcement Learning in Tensorflow

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.

Safe reinforcement learning with stability guarantees

Gaussian process modelling in Python

Fast & scalable MCMC for all your exoplanet needs!

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Gaussian Processes for Experimental Sciences

A simple, extensible library for developing AutoML systems

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

Python3 project applying Gaussian process regression for forecasting stock trends

Neural Architecture Search with Bayesian Optimisation and Optimal Transport