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abagail

The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.

acquirex

Elixir implementation of the logic for the board game Acquire. Presented at NDC Oslo 2015 - see video:

ads-server-deck

async

Async utilities for node and the browser

autocomplete

Fig adds autocomplete to your terminal.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-r

A curated list of awesome R frameworks, packages and software.

buildingmachinelearningsystemswithpython

Source Code for the book Building Machine Learning Systems with Python

cadl

Course materials/Homework materials for the Kadenze Academy course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

camanjs

Javascript HTML5 (Ca)nvas (Man)ipulation

camel-learn-reportincidents

capistrano-node-deploy

Capistrano recipe for deploying node.js apps

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

deep-learning-keras-euroscipy2016

# Deep Learning with Keras

deep-learning-models

Keras code and weights files for popular deep learning models.

deepmask

Torch implementation of DeepMask and SharpMask

discrete_optimization

dotfiles

egol

Erlang implementation of Conway's Game of Life

elixir-otp-in-action

《OTP in action》 source code rewrite into elixir.

erlyberly

erlang tracing for the masses

europython2016_dl-nlp

Introduction to Deep Learning for Natural Language Processing

event-manager

event-manager

eventemitter

Evented JavaScript for the browser

excel4j

excel4j

fp101x-content-2015

FP101x - Functional Programming MOOC 2015 Content Repository

free-programming-books

:books: Freely available programming books

galaxy

TADP UTN project

git-utils

linux scripts that facilitates git management

glpkerl

Automatically exported from code.google.com/p/glpkerl