NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.

Living document that might/will become an e-book for Parallel Computing for Data Science

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

Storage system for Deep Learning models designed using the Mochi components.

Test cases for the Mochi project.

This repository provides a set of configuration files and example scripts for running Mochi experiments on various platforms.

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

This is the implementation of Randomer Forest

NeuroData Talks

A memory efficient and fast random forest which produces trees with fast prediction speeds.

The unsupervised version of Rerf.

Build unsupervised decision trees using 2-means as the splitting method.

CERN PyHEADTAIL numerical n-body simulation code for simulating macro-particle beam dynamics with collective effects

CUDA integration for Python, plus shiny features

Machine learning for NeuroImaging in Python

Development version of Boost uBLAS