Popular GitHub Repositories for Regression Trees

100 Days of ML Coding

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

The "Python Machine Learning (1st edition)" book code repository and info resource

An open-source, low-code machine learning library in Python

A unified framework for machine learning with time series

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Postgres with GPUs for ML/AI apps.

Statistical Machine Intelligence & Learning Engine

Code for Tensorflow Machine Learning Cookbook

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

mlpack: a fast, header-only C++ machine learning library

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

:man: Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Plain python implementations of basic machine learning algorithms

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.