Stars
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
A game theoretic approach to explain the output of any machine learning model.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Companion webpage to the book "Mathematics For Machine Learning"
Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely
An open-source, low-code machine learning library in Python
Automatic extraction of relevant features from time series:
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Flax is a neural network library for JAX that is designed for flexibility.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
A scikit-learn compatible neural network library that wraps PyTorch
"Probabilistic Machine Learning" - a book series by Kevin Murphy
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Probabilistic reasoning and statistical analysis in TensorFlow
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
Bayesian optimization in PyTorch
Bayesian optimisation & Reinforcement Learning library developed by Huawei Noah's Ark Lab
Fast and Easy Infinite Neural Networks in Python
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Notebooks about Bayesian methods for machine learning
Natural Gradient Boosting for Probabilistic Prediction
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
Gaussian Process Optimization using GPy
A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.
The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a community of users and contributors by focusing initially o…
Parallel computing in Python tutorial materials