Some useful tips for faiss

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Natural Gradient Boosting for Probabilistic Prediction

Small Dataset Benchmarks on the Dataset of Datasets UCI++

PMLB: A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms.

System design patterns for machine learning

A library for efficient similarity search and clustering of dense vectors.

Interpretability and explainability of data and machine learning models

Book about interpretable machine learning

LOcal Rule-based Exlanations

An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems

A list of research papers of explainable machine learning.

Natural Intelligence is still a pretty good idea.

Model Agnostic Counterfactual Explanations

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

The data complexity library, DCoL, is a machine learning software that implements all metrics to characterize the apparent complexity of classification problems. The code is implemented in C++ and …

Extended Complexity Library in R

Code for the clustering algorithm

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Implements the Causal Forest algorithm formulated in Athey and Wager (2018).

😈Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.

Python package for Gower distance

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

A Julia machine learning framework

Fit interpretable models. Explain blackbox machine learning.