Popular GitHub Repositories for Bootstrapping Statistics

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 ;)

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

Statistical Machine Intelligence & Learning Engine

? Online machine learning in Python

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Probabilistic reasoning and statistical analysis in TensorFlow

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

Book_5_《统计至简》 | 鸢尾花书：从加减乘除到机器学习；上架！

ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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.

An open-source data logging library for machine learning models and data pipelines. ? Provides visibility into data quality & model performance over time. ?️ Supports privacy-preserving data collection, ensuring safety & robustness. ?

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Bayesian inference with probabilistic programming.

The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.