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awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-korean-nlp

A curated list of resources for NLP (Natural Language Processing) for Korean

awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

binaryconnect

Training Deep Neural Networks with binary weights during propagations

caffe

Caffe

ccw_tutor

ccw_tutorial

chatbot-rnn

A toy chatbot powered by deep learning and trained on data from Reddit

cnn-benchmarks

Benchmarks for popular CNN models

deepbio

DeepBio

deepframeworks

Evaluation of Deep Learning Frameworks

dl_tutorials_4th

dni-tensorflow

DNI(Decoupled Neural Interfaces using Synthetic Gradients) implementation with Tensorflow

dtp

fast-weights

Implementation of the paper [Using Fast Weights to Attend to the Recent Past](https://arxiv.org/abs/1610.06258)

fasttext

Library for fast text representation and classification.

ganhacks

starter from "How to Train a GAN?" at NIPS2016

gumbel

Gumbel-Softmax Variational Autoencoder with Keras

gumbel-softmax

categorical variational autoencoder using the Gumbel-Softmax estimator

igan

A deep learning software that easily generates images with a few brushstrokes (from UC Berkeley and Adobe CTL)

im2latex-tensorflow

Tensorflow implementation of the HarvardNLP paper - What You Get Is What You See: A Visual Markup Decompiler (https://arxiv.org/pdf/1609.04938v1.pdf)

kapre

kapre: Keras Audio Preprocessors

keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.

laddernet

Ladder network is a deep learning algorithm that combines supervised and unsupervised learning

medical-data

mlalgorithms

Minimal and clean examples of machine learning algorithms

models

Models built with TensorFlow

neural-style

Neural style in TensorFlow! :art:

nips14-ssl

Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"