GitHub

An end to end tutorial of a machine learning pipeline

This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial which teaches you how to "Train your own neural network" or "Learn deep ...
Hacker

How to Configure Experiments With Hydra - From an ML Enginner Perspective

Machine learning experiments require extensive parametrization, including optimizer parameters, network architecture, and data augmentation. However, we strive for concise, readable code instead of a ...
GitHub

FedML: The Community Building Open and Collaborative AI Anywhere at Any Scale

At the current stage, FedML library provides a research and production integrated edge-cloud platform for Federated/Distributed Machine Learning at anywhere at any scale. [2022/08/01] (Product ...
Frontiers

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient.
The Earth Institute Columbia University

Wasserstein Generative Adversarial Networks (WGANs) Part 1: An Introduction

Since its inception in 2014 by Goodfellow et al, generative adversarial networks (GANs) have taken the research community by storm. The business for improving GANs has grown to a point where papers on ...