CustomXGBoost

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Deep learning library in plain Numpy.

Tiny Gradient Boosting Tree

Implementation of common Data Structures and Algorithms with Go

Performance of various open source GBM implementations

CS F425 Deep Learning course at BITS Pilani (Goa Campus)

Building Decision Trees From Scratch In Python

Toy implementations of some popular ML optimizers using Python/JAX

Programmable Decision Tree Framework