Workshop to build voice enabled integration with data analytics

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

A MapBoxGL and D3 web mapping tool for exploring the dynamic population of Manhattan.

Demonstration of MapR for Industrial IoT

A high-performance tool for blockchain science and exploration

ChatterBot is a machine learning, conversational dialog engine for creating chat bots

Predict which loans will be foreclosed on.

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

A list of awesome and proven Artificial Intelligence use cases and applications

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement …

Run your web apps easily with a complete platform that you can install on any server. Build composable microservices and lambdas.

The Comprehensive Ethereum Reading List

Hugo appernetic bootstrap premium theme

Accelerate SHA256 computations in pure Go using AVX512, SHA Extensions for x86 and ARM64 for ARM. On AVX512 it provides an up to 8x improvement (over 3 GB/s per core). SHA Extensions give a perform…

Implementations of CNNs, RNNs, GANs, etc

Bayesian Program Learning model for one-shot learning

Some code I wrote for a Deep Learning class

Tools for keeping your cloud operating in top form. Chaos Monkey is a resiliency tool that helps applications tolerate random instance failures.

A simple Python script showing how the backpropagation algorithm works.

The Roslyn .NET compiler provides C# and Visual Basic languages with rich code analysis APIs.

a general purpose learning agent

"Neural Turing Machine" in Tensorflow

Collection of comprehensive TypeScript libraries for Interaction with the Ethereum JSON RPC API and utility functions.

Build native mobile apps in Go.

Code samples for my book "Neural Networks and Deep Learning"

Jupyter notebooks and code for Intro to DL talk at Genesys