Popular GitHub Repositories for Statistical Analyses

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

Bayesian Modeling and Probabilistic Programming in Python

Statistical Machine Intelligence & Learning Engine

A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.

Probabilistic reasoning and statistical analysis in TensorFlow

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage

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

A collection of research papers on decision, classification and regression trees with implementations.

Machine learning metrics for distributed, scalable PyTorch applications.

? Models for the spaCy Natural Language Processing (NLP) library

Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.

[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, exciting jailbreak methods on LLMs. It contains papers, codes, datasets, evaluations, and analyses.

A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.

Java Statistical Analysis Tool, a Java library for Machine Learning

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

R usage tips for data cleaning, data visualisation, statistical modelling and machine learning