TimeCracker

animeGAN

A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

Awesome-Deep-Learning-Resources

Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully.

CoderReader

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

crnn

Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.

dcgan_code

Deep Convolutional Generative Adversarial Networks

Deep-Learning-Papers-Reading-Roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deeplearning-papernotes

Summaries and notes on Deep Learning research papers

DeepLearningBook

MIT Deep Learning Book in PDF format

dl_course

env_config

File

Reference Files

foundations_for_deep_learning

Building a scalable foundation for deep learning

FreezeOut

Accelerate Neural Net Training by Progressively Freezing Layers

functional-zoo

PyTorch and Tensorflow functional model definitions

keras-attention-mechanism

Attention mechanism Implementation for Keras.

machine-learning-notes

机器学习笔记

nn-learn

repo for learning neural nets and related material

NotePics

image hosting for nots

odin-pytorch

Principled Detection of Out-of-Distribution Examples in Neural Networks

openbilibili

哔哩哔哩后台源码

openbilibili-1

哔哩哔哩 bilibili go-common

reinforcement-learning-an-introduction

Python code for Reinforcement Learning: An Introduction

rl4nlp

Reinforcement learning for natural language processing reading group

seq2seq

Sequence to Sequence Learning with Keras

SeqGAN

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

sequence_gan

Generative adversarial networks (GAN) applied to sequential data via recurrent neural networks (RNN).

tensorflow

Computation using data flow graphs for scalable machine learning

The-GAN-World

Everything about Generative Adversarial Networks

The-Terrible-Deep-Learning-List

15 working examples to get you started with Deep Learning without learning any of the math.