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A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
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
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
A collection of research papers on decision, classification and regression trees with implementations.
《统计学习方法》笔记-基于Python算法实现
Teaching Materials for Dr. Waleed A. Yousef
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Machine Learning library for the web and Node.
An Introduction to Statistical Learning with Applications in PYTHON
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels