Table of Contents

• Frameworks

  • Torch7
  • Theano
  • RNNLM Toolkit
  • Caffe
  • SyntaxNet - Google's syntactic parser - A TensorFlow dependency library
  • cuDNN
  • Tensorflow - Open source software library for numerical computation using data flow graphs
  • Brainstorm - Fast, flexible and fun neural networks.
  • Neon - Python based Deep Learning Framework
  • Lasagne - a lightweight library to build and train neural networks in Theano
  • Deeppy
  • nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne
  • hebel
  • Nvidia DIGITS - a web app based on Caffe
  • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
  • Minerva - a fast and flexible tool for deep learning on multi-GPU
  • convetjs
  • Mocha.jl
  • Brain
  • Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox
  • char-rnn
  • CNTK - Microsoft Cognitive Toolkit
  • Knet.jl
  • Veles - Samsung Distributed machine learning platform
  • MatConvNet: CNNs for MATLAB
  • DeepLearnToolbox
  • DMTK - Microsoft Distributed Machine Learning Tookit
  • Deepnet
  • JavaNN
  • Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)
  • Sonnet - a library for constructing neural networks by Google's DeepMind
  • deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web
  • Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework
  • OpenDL
  • RNNLM Toolkit
  • Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework
  • NeuPy - Theano based Python library for ANN and Deep Learning
  • cuda-convnet
  • RNNLIB - A recurrent neural network library
  • MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework
  • Torchnet - Torch based Deep Learning Library
  • DeepLearning4J
  • Paddle - PArallel Distributed Deep LEarning by Baidu
  • DSSTNE - Amazon's library for building Deep Learning models

• Papers

  • CMU’s list of papers
  • Batch Normalization
  • Training Recurrent Neural Networks
  • Recurrent Neural Network based Language Model
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition
  • Speech Recognition with Deep Recurrent Neural Networks
  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • LSTM
  • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  • GFRNN - supp.pdf)
  • LSTM: A Search Space Odyssey
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Visualizing and Understanding Recurrent Networks
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • Berkeley AI Research (BAIR) Laboratory
  • On Using Very Large Target Vocabulary for Neural Machine Translation
  • Attention Is All You Need
  • CMU’s list of papers
  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • Training Recurrent Neural Networks
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition
  • Speech Recognition with Deep Recurrent Neural Networks
  • On Using Very Large Target Vocabulary for Neural Machine Translation
  • Google - Sequence to Sequence Learning with Neural Networks
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • Berkeley AI Research (BAIR) Laboratory
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • GFRNN - supp.pdf)
  • LSTM: A Search Space Odyssey
  • Visualizing and Understanding Recurrent Networks

• Free Online Books

  • Neural Networks and Deep Learning
  • Deep Learning
  • Deep Learning in Neural Networks: An Overview
  • neuraltalk - based RNN/LSTM implementation
  • Neural Networks and Deep Learning
  • Deep Learning in Neural Networks: An Overview

• Miscellaneous

  • An efficient, batched LSTM.
  • AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
  • Machine Learning for Software Engineers
  • Google deepdream - Neural Network art
  • Dockerface
  • 100 Best Github Resources in Github for DL
  • Word2Vec

• Tutorials

  • Theano Tutorial
  • Torch7 Tutorials
  • Deep Learning with Python
  • Deep Learning from the Bottom up
  • TensorFlow Python Notebooks
  • TensorFlow tutorials
  • More TensorFlow tutorials
  • Pytorch Tutorial by Yunjey Choi
  • TensorFlow-World
  • Keras and Lasagne Deep Learning Tutorials
  • A Deep Learning Tutorial: From Perceptrons to Deep Networks
  • Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder

• Datasets

  • Image QA
  • Columbia-Utrecht Reflectance and Texture Database - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)
  • Ubuntu Dialogue Corpus - Dialogs of ubuntu journal.
  • 微博数据集 - 华为李航实验室发布，也是论文“Neural Responding Machine for Short-Text Conversation”使用的数据集。
  • 微博数据集 - 新浪微博数据集，评论回复短句。
  • OpenSubtitles - Movie subtitles.
  • DeepMind QA Corpus - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. [Paper](http://arxiv.org/abs/1506.03340) for reference.
  • Twitter - Dataset from twitter.
  • Papaya Conversational Data Set - Reorganized based on Cornell and Reddit.
  • 白鹭时代中文问答语料 - 白鹭时代论坛问答数据，一个问题对应一个最好的答案。
  • dgk_shooter_min.conv - Chinese movie dialogs.
  • 微博数据集 - 新浪微博数据集，评论回复短句。

• WebSites

  • deeplearning.net
  • deeplearning.stanford.edu
  • eecs.umich.edu/ai
  • cgi.cse.unsw.edu.au/~aishare
  • isi.edu/AI/isd.htm
  • deeplearning.cs.toronto.edu
  • Guide to Machine Learning
  • cs.brown.edu/research/ai
  • Deep Learning News
  • deeplearning.stanford.edu
  • eecs.umich.edu/ai
  • Deep Learning for Beginners
  • ai.sri.com
  • Machine Learning is Fun! Adam Geitgey's Blog
  • Guide to Machine Learning
  • AI Weekly
  • csail.mit.edu

• Courses

  • Machine Learning - Stanford - 2014)
  • Machine Learning - Carnegie Mellon
  • Tensorflow for Deep Learning Research
  • Machine Learning - Carnegie Mellon

• Videos and Lectures

  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning

• Conferences

  • IJCAI - International Joint Conference on Artificial Intelligence
  • ECML - European Conference on Machine Learning
  • NIPS - Neural Information Processing Systems
  • ICML - International Conference on Machine Learning
  • KDD - Knowledge Discovery and Data Mining
  • ECML - European Conference on Machine Learning