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algorithms-nutshell-2ed icon algorithms-nutshell-2ed

Code Repository associated with "Algorithms In A Nutshell", 2ed, published by O'Reilly Media, Inc. http://shop.oreilly.com/product/0636920032885.do

anese icon anese

A NES Emulator written for fun and learning - first implementation of wideNES

dask icon dask

Parallel computing with task scheduling

desktop icon desktop

Simple collaboration from your desktop

fasttext icon fasttext

Library for fast text representation and classification.

food2vec icon food2vec

:hamburger: Live recipe recommendation demo: https://altosaar.github.io/food2vec/ Pretty plots: https://jaan.io/food2vec-augmented-cooking-machine-intelligence/

fraud_detector icon fraud_detector

Fraud Detection using ensemble of Statistical, Network analysis and Machine learning approach.

lexvec icon lexvec

This is an implementation of the LexVec word embedding model (similar to word2vec and GloVe) that achieves state of the art results in multiple NLP tasks

mldb icon mldb

MLDB is the Machine Learning Database

mxnet icon mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

nupic icon nupic

Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.

pattern icon pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

pattern_classification icon pattern_classification

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

pifuhd icon pifuhd

High-Resolution 3D Human Digitization from A Single Image.

pix2pixhd icon pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

shogun icon shogun

The Shogun Machine Learning Toolbox (Source Code)

siggraph2016_colorization icon siggraph2016_colorization

Code for the paper 'Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification'.

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