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edusoho

EduSoho 是教育机构转型在线教育的一站式解决方案，让教育机构可以零门槛搭建和运营独立网校，成功转型在线教育。截止2017年4月，国内外已经有4万多家教育机构和个人创业者通过 EduSoho 转型在线教育。

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

encfsmp

Mount EncFS folders - multiplatform style.

epicbox

Run untrusted code in secure Docker based sandboxes

eventbrite-tools

Eventbrite bot for securing tickets and getting all hidden ticket information.

expedialearningtorank

Expedia Learning to Rank Hotels to Maximize Purchases, Kaggle Competition

exxon.github.io

Exxon Mobil

facebook.github.io

Facebook

factorization-machine-for-prediction

Factorization Machine for regression and classification

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit

faiss

A library for efficient similarity search and clustering of dense vectors.

fastfm

fastFM: A Library for Factorization Machines

fasttext

Library for fast text representation and classification.

fb.resnet.torch

Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts

filodb

Distributed. Columnar. Versioned. Streaming. SQL.

finalseg

Chinese Words Segment Library based on HMM model

foodtrucks

:truck: San Francisco's finger-licking street food now at your fingertips.

forestjs

Random Forest implementation for JavaScript. Supports arbitrary weak learners. Includes interactive demo.

fotoratan

Simple image Gallery Hosted on Google App Engine

framework

Machine learning, computer vision, statistics and general scientific computing for .NET

frrn

Full Resolution Residual Networks for Semantic Image Segmentation

fullscreenmario

A free HTML5 remake of Nintendo's original Super Mario Bros. It includes the original 32 levels, a random map generator, a level editor, and over a dozen custom mods.

gan

some demo of GANs

geek-programming-books

Free programing ebooks

gensim

Topic Modelling for Humans

gm.github.io

General Motors

google-cloud-storage-uploader

google.github.io

Google

gradientdescentexample

Example demonstrating how gradient descent may be used to solve a linear regression problem

grequests

Requests + Gevent = <3