Tutorials

Demystifying Deep Reinforcement Learning (Part1)

http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/

Deep Reinforcement Learning With Neon (Part2)

http://neuro.cs.ut.ee/deep-reinforcement-learning-with-neon/

Deep Reinforcement Learning

• intro: David Silver, Google DeepMind
• slides: http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf
• mirror: http://pan.baidu.com/s/1qWBOJGo

Deep Reinforcement Learning

• intro: MLSS 2016. John Schulman[UC Berkeley]
• homepage: http://rl-gym-doc.s3-website-us-west-2.amazonaws.com/mlss/index.html
• slides: http://pan.baidu.com/s/1jIatusA#path=%252F

Deep Reinforcement Learning: Pong from Pixels

• intro: Andrej Karpathy
• blog: http://karpathy.github.io/2016/05/31/rl/
• gist: https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5

Deep Reinforcement Learning

• instructor: David Silver. RLDM 2015
• video: http://videolectures.net/rldm2015_silver_reinforcement_learning/

Deep Reinforcement Learning

• intro: David Silver [Google DeepMind]
• video: http://techtalks.tv/talks/deep-reinforcement-learning/62360/
• slides: http://hunch.net/~beygel/deep_rl_tutorial.pdf

The Nuts and Bolts of Deep RL Research

• intro: NIPS 2016, John Schulman, OpenAI
• slides: http://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf
• mirror: https://pan.baidu.com/s/1kVkBLkF

ML Tutorial: Modern Reinforcement Learning and Video Games

• intro: by Marc Bellemare [DeepMind]
• youtube: https://www.youtube.com/watch?v=WuFMrk3ZbkE
• mirror: https://www.bilibili.com/video/av17360035/

Reinforcement learning explained

• blog: https://www.oreilly.com/ideas/reinforcement-learning-explained

Beginner’s guide to Reinforcement Learning & its implementation in Python

https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/

Reinforcement Learning on the Web

• intro: Andrej Karpathy
• slides: https://docs.google.com/presentation/d/1lcYrN56V2_SuX1rSmpzOUeMnheF6Jsu33-MsvLW9O_4/edit#slide=id.p
• slides: http://alpha.openai.com/ak_rework_2017.pdf

Deep Q Learning with Keras and Gym

• blog: https://keon.io/rl/deep-q-learning-with-keras-and-gym/
• github: https://github.com/keon/deep-q-learning

“Deep Reinforcement Learning, Decision Making, and Control

• intro: ICML 2017 Tutorial
• slides: https://sites.google.com/view/icml17deeprl

Simple Reinforcement Learning with Tensorflow

Part 0: Q-Learning with Tables and Neural Networks https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.oo105wa2t

Part 1 - Two-armed Bandit

https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149#.tk89k51ob

Part 2 - Policy-based Agents

https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724#.n2wytg9q0

Part 3 - Model-Based RL https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99#.742i2yj6p

Part 4: Deep Q-Networks and Beyond https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df#.jox069crz

Part 5: Visualizing an Agent’s Thoughts and Actions https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-5-visualizing-an-agents-thoughts-and-actions-4f27b134bb2a#.pluh6cygm

Part 6: Partial Observability and Deep Recurrent Q-Networks

• blog: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc#.3se46qkzy
• github: https://gist.github.com/awjuliani/35d2ab3409fc818011b6519f0f1629df

Part 7: Action-Selection Strategies for Exploration

• blog: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-7-action-selection-strategies-for-exploration-d3a97b7cceaf#.8mcaa5nbe
• demo: https://awjuliani.github.io/exploration/index.html

Dissecting Reinforcement Learning

• part 1: https://mpatacchiola.github.io/blog/2016/12/09/dissecting-reinforcement-learning.html
• part 2: https://mpatacchiola.github.io/blog/2017/01/15/dissecting-reinforcement-learning-2.html
• part 3: https://mpatacchiola.github.io/blog/2017/01/29/dissecting-reinforcement-learning-3.html
• github: https://github.com/mpatacchiola/dissecting-reinforcement-learning

REINFORCE tutorial

• intro: A small collection of code snippets and notes explaining the foundations of the REINFORCE algorithm.
• github: https://github.com/mathias-madsen/reinforce_tutorial

Deep Q-Learning Recap

http://blog.davidqiu.com/Research/%5B%20Recap%20%5D%20Deep%20Q-Learning%20Recap/

Introduction to Reinforcement Learning

• intro: Joelle Pineau [McGill University]
• video: http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/
• slides: http://videolectures.net/site/normal_dl/tag=1051677/deeplearning2016_pineau_reinforcement_learning_01.pdf

Courses

Advanced Topics: RL

UCL Course on RL

• instructors: David Silver (Google DeepMind, AlphaGo)
• homepage: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
• youtube: https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa
• video: http://pan.baidu.com/s/1bnWGuIz/
• assignment: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/Easy21-Johannes.pdf

CS 294: Deep Reinforcement Learning, Fall 2017

• instructor: Sergey Levine
• homepage: http://rll.berkeley.edu/deeprlcourse/
• youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3
• bilibili: https://www.bilibili.com/video/av21501169/

CS 294: Deep Reinforcement Learning, Spring 2017

• course page: http://rll.berkeley.edu/deeprlcoursesp17/
• github: https://github.com//txizzle/drl

Berkeley CS 294: Deep Reinforcement Learning

• instructors: John Schulman, Pieter Abbeel
• homepage: http://rll.berkeley.edu/deeprlcourse/
• youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX
• mirror: https://pan.baidu.com/s/1hsQcm1Y

(Udacity) Reinforcement Learning - Offered at Georgia Tech as CS 8803

• instructor: Charles Isbell, Michael Littman
• homepage: https://www.udacity.com/course/reinforcement-learning–ud600
• homepage: https://classroom.udacity.com/courses/ud820/lessons/684808907/concepts/6512308530923

CS229 Lecture notes Part XIII: Reinforcement Learning and Control

• intro: Andrew Ng
• lecture notes: http://cs229.stanford.edu/notes/cs229-notes12.pdf

Practical_RL: A course in reinforcement learning in the wild

• github: https://github.com/yandexdataschool/Practical_RL

Reinforcement Learning (COMP-762) Winter 2017

• course page: http://www.cs.mcgill.ca/~dprecup/courses/rl.html
• lectures: http://www.cs.mcgill.ca/~dprecup/courses/RL/lectures.html

• lectures: https://sites.google.com/view/deep-rl-bootcamp/lectures
• video: https://www.bilibili.com/video/av15568836/

CMPUT 366: Intelligent Systems and CMPUT 609: Reinforcement Learning & Artificial Intelligence

• intro: by Rich Sutton, Adam White
• lecture video: https://drive.google.com/drive/folders/0B3w765rOKuKAMG9lbmRacFdsLWM?direction=a

Deep Reinforcement Learning and Control (Spring 2017, CMU 10703)

• instructors: Katerina Fragkiadaki, Ruslan Satakhutdinov
• homepage: https://katefvision.github.io/
• video: https://www.youtube.com/playlist?list=PLpIxOj-HnDsNPFdu2UqCu2McJKHs-eWXv
• mirror: https://www.bilibili.com/video/av18865689/

Papers

Playing Atari with Deep Reinforcement Learning

• intro: Google DeepMind. NIPS Deep Learning Workshop 2013
• arxiv: http://arxiv.org/abs/1312.5602
• github: https://github.com/kristjankorjus/Replicating-DeepMind
• demo: http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
• github: https://github.com/Kaixhin/Atari
• github(Tensorflow): https://github.com/gliese581gg/DQN_tensorflow
• summary: https://github.com/aleju/papers/blob/master/neural-nets/Playing_Atari_with_Deep_Reinforcement_Learning.md

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

• intro: NIPS 2014
• keywords: DQN, MCTS
• paper: http://papers.nips.cc/paper/5421-scalable-inference-for-neuronal-connectivity-from-calcium-imaging
• paper: https://web.eecs.umich.edu/~baveja/Papers/UCTtoCNNsAtariGames-FinalVersion.pdf

Replicating the Paper “Playing Atari with Deep Reinforcement Learning”

• intro: University of Tartu
• technical report: https://courses.cs.ut.ee/MTAT.03.291/2014_spring/uploads/Main/Replicating%20DeepMind.pdf

A Tutorial for Reinforcement Learning

• paper: http://web.mst.edu/~gosavia/tutorial.pdf
• code(C): http://web.mst.edu/~gosavia/bookcodes.html
• code(Matlab): http://web.mst.edu/~gosavia/mrrl_website.html

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

• arxiv: http://arxiv.org/abs/1507.00814
• notes: https://www.evernote.com/shard/s189/sh/a4262b84-a322-4f77-9a76-569278be84af/b8c3e146a76ca3853f560bb03b60a481

Massively Parallel Methods for Deep Reinforcement Learning

• intro: ICML 2015. DeepMind
• keywords: DQN, Gorila
• arxiv: https://arxiv.org/abs/1507.04296

Action-Conditional Video Prediction using Deep Networks in Atari Games

• homepage: https://sites.google.com/a/umich.edu/junhyuk-oh/action-conditional-video-prediction
• arxiv: http://arxiv.org/abs/1507.08750
• github: https://github.com/junhyukoh/nips2015-action-conditional-video-prediction
• video: http://video.weibo.com/show?fid=1034:98062f3d83e41da6faa99cde5aa1ac97

Deep Recurrent Q-Learning for Partially Observable MDPs

• intro: AAAI 2015
• arxiv: https://arxiv.org/abs/1507.06527

Continuous control with deep reinforcement learning

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1509.02971
• github: https://github.com/iassael/torch-policy-gradient
• github: https://github.com/stevenpjg/ddpg-aigym
• github(TensorFlow + OpenAI Gym): https://github.com/SimonRamstedt/ddpg

Benchmarking for Bayesian Reinforcement Learning

• arxiv: http://arxiv.org/abs/1509.04064
• code: https://github.com/mcastron/BBRL/
• reading: http://blogs.ulg.ac.be/damien-ernst/benchmarking-for-bayesian-reinforcement-learning/

Deep Reinforcement Learning with Double Q-learning

• intro: AAAI 2016
• arxiv: https://arxiv.org/abs/1509.06461

Giraffe: Using Deep Reinforcement Learning to Play Chess

• arxiv: http://arxiv.org/abs/1509.01549

Human-level control through deep reinforcement learning

• intro: Google DeepMind. 2015 Nature
• paper: http://www.readcube.com/articles/10.1038/nature14236?shared_access_token=Lo_2hFdW4MuqEcF3CVBZm9RgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkApOr3ZSsJAldp-tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D
• paper: http://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf
• github(Lua/Torch): https://github.com/deepmind/dqn
• mirror: http://pan.baidu.com/s/1kTiwzOF
• code: https://sites.google.com/a/deepmind.com/dqn/
• youtube: https://www.youtube.com/watch?v=V2wzkPmiB_A
• github: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
• github: https://github.com/tambetm/simple_dqn
• github: https://github.com/devsisters/DQN-tensorflow
• reddit: https://www.reddit.com/r/MachineLearning/comments/2x4yy1/google_deepmind_nature_paper_humanlevel_control

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

• arxiv: http://arxiv.org/abs/1510.02173

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1509.08731
• notes: https://www.evernote.com/shard/s189/sh/8c7ff9d9-c321-4e83-a802-58f55ebed9ac/bfc614113180a5f4624390df56e73889

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06342
• github: https://github.com/eparisotto/ActorMimic

MazeBase: A Sandbox for Learning from Games

• intro: New York University & Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.07401

Learning Simple Algorithms from Examples

• intro: New York University & Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.07275
• github: https://github.com/wojzaremba/algorithm-learning

Learning Algorithms from Data

• PhD thesis: http://www.cs.nyu.edu/media/publications/zaremba_wojciech.pdf
• github: https://github.com/wojzaremba/algorithm-learning

Multiagent Cooperation and Competition with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1511.08779
• github: https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player

Active Object Localization with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1511.06015

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions

• arxiv: http://arxiv.org/abs/1512.01124

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

• arxiv: http://arxiv.org/abs/1512.02011

State of the Art Control of Atari Games Using Shallow Reinforcement Learning

• arxiv: http://arxiv.org/abs/1512.01563

Angrier Birds: Bayesian reinforcement learning

• arxiv: http://arxiv.org/abs/1601.01297
• github: https://github.com/imanolarrieta/angrybirds
• gitxiv: http://gitxiv.com/posts/Nr2N7j4YrR4gnCYK9/angrier-birds-bayesian-reinforcement-learning

Prioritized Experience Replay

• arxiv: http://arxiv.org/abs/1511.05952

Dueling Network Architectures for Deep Reinforcement Learning

• intro: ICML 2016 best paper
• arxiv: http://arxiv.org/abs/1511.06581
• notes: https://hadovanhasselt.wordpress.com/2016/06/20/best-paper-at-icml-dueling-network-architectures-for-deep-reinforcement-learning/

Asynchronous Methods for Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1602.01783
• github(Tensorflow): https://github.com/traai/async-deep-rl
• github(Tensorflow+Keras+OpenAI Gym): https://github.com/coreylynch/async-rl
• github(Tensorflow): https://github.com/devsisters/async-rl-tensorflow
• github(PyTorch): https://github.com/ikostrikov/pytorch-a3c
• notes: https://blog.acolyer.org/2016/10/10/asynchronous-methods-for-deep-reinforcement-learning/

Graying the black box: Understanding DQNs

• arxiv: http://arxiv.org/abs/1602.02658

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

• arxiv: http://arxiv.org/abs/1602.02672

Value Iteration Networks

• intro: NIPS 2016, Best Paper Award. University of California, Berkeley
• arxiv: http://arxiv.org/abs/1602.02867
• github(official, Theano): https://github.com/avivt/VIN
• github: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks
• github: https://github.com/onlytailei/PyTorch-value-iteration-networks
• github: https://github.com/kentsommer/pytorch-value-iteration-networks
• github: https://github.com/neka-nat/vin-keras
• notes(by Andrej Karpathy): https://github.com/karpathy/paper-notes/blob/master/vin.md

Insights in Reinforcement Learning

• intro: MSc thesis
• mirror: http://pan.baidu.com/s/1bn51BYJ

Using Deep Q-Learning to Control Optimization Hyperparameters

• arxiv: http://arxiv.org/abs/1602.04062

Continuous Deep Q-Learning with Model-based Acceleration

• arxiv: http://arxiv.org/abs/1603.00748

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

• arxiv: http://arxiv.org/abs/1603.01121

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

• intro: MIT
• arxiv: https://arxiv.org/abs/1604.06057
• github: https://github.com/EthanMacdonald/h-DQN

Benchmarking Deep Reinforcement Learning for Continuous Control

• arxiv: http://arxiv.org/abs/1604.06778
• github: https://github.com/rllab/rllab
• doc: https://rllab.readthedocs.org/en/latest/

Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning

• homepage: http://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/index.html
• paper: http://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/2016-TOG-deepRL.pdf
• github: https://github.com/xbpeng/DeepTerrainRL

Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks

• arxiv: http://arxiv.org/abs/1605.05359

Deep Successor Reinforcement Learning (MIT)

• arxiv: http://arxiv.org/abs/1606.02396
• github: https://github.com/Ardavans/DSR

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

• arxiv: https://arxiv.org/abs/1605.06676
• github: https://github.com/iassael/learning-to-communicate

Deep Reinforcement Learning with Regularized Convolutional Neural Fitted Q Iteration RC-NFQ: Regularized Convolutional Neural Fitted Q Iteration

• intro: A batch algorithm for deep reinforcement learning. Incorporates dropout regularization and convolutional neural networks with a separate target Q network.
• paper: http://machineintelligence.org/papers/rc-nfq.pdf
• github: https://github.com/cosmoharrigan/rc-nfq

Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1609.02993

Bayesian Reinforcement Learning: A Survey

• arxiv: http://arxiv.org/abs/1609.04436

Playing FPS Games with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1609.05521
• demo: https://www.youtube.com/playlist?list=PLduGZax9wmiHg-XPFSgqGg8PEAV51q1FT
• notes: https://blog.acolyer.org/2016/11/23/playing-fps-games-with-deep-reinforcement-learning/

Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States

• intro: University of Washington & UC Berkeley
• arxiv: https://arxiv.org/abs/1610.01112

Utilization of Deep Reinforcement Learning for saccadic-based object visual search

• arxiv: https://arxiv.org/abs/1610.06492

Learning to Navigate in Complex Environments

• intro: Google DeepMind
• arxiv: https://arxiv.org/abs/1611.03673
• github: https://github.com/deepmind/lab
• youtube: https://www.youtube.com/watch?v=lNoaTyMZsWI

Reinforcement Learning with Unsupervised Auxiliary Tasks

• intro: DeepMind. ICLR 2017 oral
• arxiv: https://arxiv.org/abs/1611.05397

Learning to reinforcement learn

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1611.05763

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

• intro: Graduate Training Center of Neuroscience & MSR
• arxiv: https://arxiv.org/abs/1611.07078

Exploration for Multi-task Reinforcement Learning with Deep Generative Models

• intro: NIPS Deep Reinforcement Learning Workshop 2016
• arxiv: https://arxiv.org/abs/1611.09894

Neural Combinatorial Optimization with Reinforcement Learning

• intro: Google Brain
• keywords: traveling salesman problem (TSP)
• arxiv: https://arxiv.org/abs/1611.09940

Loss is its own Reward: Self-Supervision for Reinforcement Learning

• arxiv: https://arxiv.org/abs/1612.07307

Reinforcement Learning Using Quantum Boltzmann Machines

• intro: 1QB Information Technologies (1QBit)
• arxiv: https://arxiv.org/abs/1612.05695

Deep Reinforcement Learning applied to the game Bubble Shooter

• bachelor thesis: https://staff.fnwi.uva.nl/b.bredeweg/pdf/BSc/20152016/Samson.pdf
• github: https://github.com/laurenssam/AlphaBubble
• demo: https://www.youtube.com/watch?v=DPAKFenNgbs

Deep Reinforcement Learning: An Overview

• arxiv: https://arxiv.org/abs/1701.07274

Robust Adversarial Reinforcement Learning

• intro: CMU & Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1703.02702

Beating Atari with Natural Language Guided Reinforcement Learning

• intro: Stanford University
• arxiv: https://arxiv.org/abs/1704.05539

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1705.06769
• github: https://github.com/Nat-D/FeatureControlHRL

Distral: Robust Multitask Reinforcement Learning

• intro: DeepMind
• keywords: Distill, transfer learning
• arxiv: https://arxiv.org/abs/1707.04175

Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations

• intro: Syracuse University & University of California, Riverside
• arxiv: https://arxiv.org/abs/1710.03792

Robust Deep Reinforcement Learning with Adversarial Attacks

https://arxiv.org/abs/1712.03632

Variational Deep Q Network

• intro: Second workshop on Bayesian Deep Learning (NIPS 2017). Columbia University
• arxiv: https://arxiv.org/abs/1711.11225

On Monte Carlo Tree Search and Reinforcement Learning

https://www.jair.org/media/5507/live-5507-10333-jair.pdf

Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

• intro: deepsense.ai & Intel & Polish Academy of Sciences
• arxiv: https://arxiv.org/abs/1801.02852
• gihtub: https://github.com//anonymous-author1/DDRL

Surveys

Reinforcement Learning: A Survey

• intro: JAIR 1996
• project page: http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/rl-survey.html
• arxiv: http://arxiv.org/abs/cs/9605103

A Brief Survey of Deep Reinforcement Learning

• intro: IEEE Signal Processing Magazine, Special Issue on Deep Learning for Image Understanding
• intro: Imperial College London & Arizona State University
• arxiv: https://arxiv.org/abs/1708.05866

Playing Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

• arxiv: http://arxiv.org/abs/1605.02097
• github: https://github.com/Marqt/ViZDoom
• homepage: http://vizdoom.cs.put.edu.pl/
• tutorial: http://vizdoom.cs.put.edu.pl/tutorial

Deep Reinforcement Learning From Raw Pixels in Doom

• intro: Bachelor’s thesis
• arxiv: https://arxiv.org/abs/1610.02164

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1612.00380

Reinforcement Learning via Recurrent Convolutional Neural Networks

• intro: ICPR 2016
• arxiv: https://arxiv.org/abs/1701.02392
• github: https://github.com/tanmayshankar/RCNN_MDP

Shallow Updates for Deep Reinforcement Learning

• intro: The Technion & UC Berkeley
• arxiv: https://arxiv.org/abs/1705.07461
• github(Official): https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL

Projects

TorchQLearning

• github: https://github.com/SeanNaren/TorchQLearningExample

General_Deep_Q_RL: General deep Q learning framework

• github: https://github.com/VinF/General_Deep_Q_RL
• wiki: https://github.com/VinF/General_Deep_Q_RL/wiki

Snake: Toy example of deep reinforcement model playing the game of snake

• github: https://github.com/bitwise-ben/Snake

Using Deep Q Networks to Learn Video Game Strategies

• github: https://github.com/asrivat1/DeepLearningVideoGames

qlearning4k: Q-learning for Keras

• intro: “Qlearning4k is a reinforcement learning add-on for the python deep learning library Keras. Its simple, and is ideal for rapid prototyping.”
• github: https://github.com/farizrahman4u/qlearning4k

rlenvs: Reinforcement learning environments for Torch7, inspired by RL-Glue

• github: https://github.com/Kaixhin/rlenvs

deep_rl_ale: An implementation of Deep Reinforcement Learning / Deep Q-Networks for Atari games in TensorFlow

• github: https://github.com/Jabberwockyll/deep_rl_ale

Chimp: General purpose framework for deep reinforcement learning

• github: https://github.com/sisl/Chimp

Deep Q Learning for ATARI using Tensorflow

• github: https://github.com/mrkulk/deepQN_tensorflow

DeepQLearning: A powerful machine learning algorithm utilizing Q-Learning and Neural Networks, implemented using Torch and Lua.

• github: https://github.com/blakeMilner/DeepQLearning

OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms

• homepage: https://gym.openai.com/
• github: https://github.com/openai/gym

DeeR: DEEp Reinforcement learning framework

• github: https://github.com/VinF/deer/
• docs: http://deer.readthedocs.io/en/latest/

KeRLym: A Deep Reinforcement Learning Toolbox in Keras

• homepage: https://oshearesearch.com/index.php/2016/06/14/kerlym-a-deep-reinforcement-learning-toolbox-in-keras/
• github: https://github.com/osh/kerlym

Pack of Drones: Layered reinforcement learning for complex behaviors

• github: https://github.com/MickyDowns/deep-theano-rnn-lstm-car
• youtube: https://www.youtube.com/watch?v=WrLRGzbfeZc

RL Helicopter Game: Q-Learning and DQN Reinforcement Learning to play the Helicopter Game - Keras based!

• project page: http://dandxy89.github.io/rf_helicopter/
• github: https://github.com/dandxy89/rf_helicopter

Playing Mario with Deep Reinforcement Learning

• github: https://github.com/aleju/mario-ai

Deep Attention Recurrent Q-Network

• intro: Deep Reinforcement Learning Workshop, NIPS 2015. DeepHack Game
• arxiv: https://arxiv.org/abs/1512.01693
• github: https://github.com/5vision/DARQN

Deep Reinforcement Learning in TensorFlow

• intro: TensorFlow implementation of Deep Reinforcement Learning papers
• github: https://github.com/carpedm20/deep-rl-tensorflow

rltorch: A RL package for Torch that can also be used with openai gym

• github: https://github.com/ludc/rltorch

deep_q_rl: Theano-based implementation of Deep Q-learning

• github: https://github.com/spragunr/deep_q_rl

Reinforcement-trading

• intro: This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.
• github: https://github.com/deependersingla/deep_trader

dist-dqn：Distributed Reinforcement Learning using Deep Q-Network in TensorFlow

• github: https://github.com/viswanathgs/dist-dqn

Deep Reinforcement Learning for Keras

• github: https://github.com/matthiasplappert/keras-rl

RL4J: Reinforcement Learning for the JVM

• intro: Reinforcement learning framework integrated with deeplearning4j.
• github: https://github.com/deeplearning4j/rl4j

Teaching Your Computer To Play Super Mario Bros. – A Fork of the Google DeepMind Atari Machine Learning Project

• blog: http://www.ehrenbrav.com/2016/08/teaching-your-computer-to-play-super-mario-bros-a-fork-of-the-google-deepmind-atari-machine-learning-project/
• github: https://github.com/ehrenbrav/DeepQNetwork

dprl: Deep reinforcement learning package for torch7

• github: https://github.com/PoHsunSu/dprl

Reinforcement Learning for Torch: Introducing torch-twrl

• blog: https://blog.twitter.com/2016/reinforcement-learning-for-torch-introducing-torch-twrl
• github: https://github.com/twitter/torch-twrl

Alpha Toe - Using Deep learning to master Tic-Tac-Toe - Daniel Slater

• blog: http://www.danielslater.net/2016/10/alphatoe.html
• youtube: https://www.youtube.com/watch?v=Meb5hApAnj4
• github: https://github.com/DanielSlater/AlphaToe

Tensorflow-Reinforce: Implementation of Reinforcement Learning Models in Tensorflow

• github: https://github.com/yukezhu/tensorflow-reinforce

deep RL hacking on minecraft with malmo

• github: https://github.com/matpalm/malmomo

ReinforcementLearning

• intro: MC control, Q-learning, SARSA, Cross Entropy Method
• github: https://github.com/janivanecky/ReinforcementLearning

markovjs: Reinforcement Learning in JavaScript

• github: https://github.com/lsunsi/markovjs

Deep Q: Deep reinforcement learning with TensorFlow

• github: https://github.com/tobegit3hub/deep_q

Deep Q-Learning Network in pytorch

https://github.com/transedward/pytorch-dqn

Tensorflow-RL: Implementations of deep RL papers and random experimentation

https://github.com/steveKapturowski/tensorflow-rl

Minimal and Clean Reinforcement Learning Examples

https://github.com/rlcode/reinforcement-learning

DeepRL: Highly modularized implementation of popular deep RL algorithms by PyTorch

https://github.com/ShangtongZhang/DeepRL

Autonomous vehicle navigation

Self-Driving-Car-AI

• intro: A simple self-driving car AI python script using the deep Q-learning algorithm
• github: https://github.com//JianyangZhang/Self-Driving-Car-AI

Autonomous vehicle navigation based on Deep Reinforcement Learning

https://github.com//kaihuchen/DRL-AutonomousVehicles

Car Racing using Reinforcement Learning

• intro: Stanford University
• paper: https://web.stanford.edu/class/cs221/2017/restricted/p-final/elibol/final.pdf

Play Flappy Bird

Using Deep Q-Network to Learn How To Play Flappy Bird

• github: https://github.com/yenchenlin/DeepLearningFlappyBird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)

• blog: http://blog.csdn.net/songrotek/article/details/50951537
• github: https://github.com/songrotek/DRL-FlappyBird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN)

• github: https://github.com/li-haoran/DRL-FlappyBird

MXNET-Scala Playing Flappy Bird Using Deep Reinforcement Learning

• github: https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/DRLFlappyBird

Flappy Bird Bot using Reinforcement Learning in Python

• github: https://github.com/chncyhn/flappybird-qlearning-bot

Using Keras and Deep Q-Network to Play FlappyBird

• blog: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html
• github: https://github.com/yanpanlau/Keras-FlappyBird

Pong

Building a Pong playing AI in just 1 hour(plus 4 days training…)

• sildes: https://speakerdeck.com/danielslater/building-a-pong-ai
• github: https://github.com/DanielSlater/PyDataLondon2016
• youtube: https://www.youtube.com/watch?v=n8NdT_3y9oY

Pong Neural Network(LIVE)

• youtube: https://www.youtube.com/watch?v=Hqf__FlRlzg
• github: https://github.com/llSourcell/pong_neural_network_live

Tips and Tricks

DeepRLHacks

• intro: The Nuts and Bolts of Deep RL Research
• github: https://github.com/williamFalcon/DeepRLHacks

Library

BURLAP: Brown-UMBC Reinforcement Learning and Planning (BURLAP) java code library

• intro: for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them
• homepage: http://burlap.cs.brown.edu/

AgentNet: Deep Reinforcement Learning library for humans

• intro: A lightweight library to build and train deep reinforcement learning and custom recurrent networks using Theano+Lasagne
• github: https://github.com/yandexdataschool/AgentNet

Atari Multitask & Transfer Learning Benchmark (AMTLB)

• intro: Atari gauntlet for RL agents
• project page: http://ai-on.org/projects/multitask-and-transfer-learning.html
• github: https://github.com/deontologician/atari_multitask

Coach: a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms

• intro: Reinforcement Learning Coach by Intel® Nervana™ enables easy experimentation with state of the art Reinforcement Learning algorithms
• homepage: http://coach.nervanasys.com/
• github: https://github.com/NervanaSystems/coach

Blogs

A Short Introduction To Some Reinforcement Learning Algorithms

http://webdocs.cs.ualberta.ca/~vanhasse/rl_algs/rl_algs.html

A Painless Q-Learning Tutorial

http://mnemstudio.org/path-finding-q-learning-tutorial.htm

Reinforcement Learning - Part 1

http://outlace.com/Reinforcement-Learning-Part-1/

Reinforcement Learning - Monte Carlo Methods

http://outlace.com/Reinforcement-Learning-Part-2/

Q-learning with Neural Networks

http://outlace.com/Reinforcement-Learning-Part-3/

Guest Post (Part I): Demystifying Deep Reinforcement Learning

http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

Using reinforcement learning in Python to teach a virtual car to avoid obstacles: An experiment in Q-learning, neural networks and Pygame.

• blog: https://medium.com/@harvitronix/using-reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-6e782cc7d4c6#.p8ug6snri
• github: https://github.com/harvitronix/reinforcement-learning-car

Reinforcement learning in Python to teach a virtual car to avoid obstacles — part 2

https://medium.com/@harvitronix/reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-part-2-93e614fcd238#.i0o643m1h

Some Reinforcement Learning Algorithms in Python, C++

• pan: http://pan.baidu.com/s/1mhcYf3M#path=%252FImplementations%2520of%2520Some%2520Reinforcement%2520Learning%2520Algorithms

learning to do laps with reinforcement learning and neural nets

• blog: http://matpalm.com/blog/drivebot/
• github: https://github.com/matpalm/drivebot

Get a taste of reinforcement learning — implement a tic tac toe agent

https://medium.com/@shiyan/get-a-taste-of-reinforcement-learning-implement-a-tic-tac-toe-agent-deda5617b2e4#.59bx71a2h

Best reinforcement learning libraries?

• reddit: https://www.reddit.com/r/MachineLearning/comments/4b2ugc/best_reinforcement_learning_libraries/

Super Simple Reinforcement Learning Tutorial

• part 1: https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149
• part 2: https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724#.dyhxww1u6
• part 3: https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99#.r4c7i7tjq
• gist: https://gist.github.com/awjuliani/16608e1c4968baaa692b9b8c7dd94d04

Reinforcement Learning in Python

• github: https://github.com/NathanEpstein/pydata-reinforce

The Skynet Salesman

• keyworkds: traveling salesman problem (TSP), deep Q learning
• blog: http://multithreaded.stitchfix.com/blog/2016/07/21/skynet-salesman/
• github: https://github.com/jn2clark/ReinforcementLearning/tree/master/DeepQ

Apprenticeship learning using Inverse Reinforcement Learning

• blog: https://jangirrishabh.github.io/2016/07/09/virtual-car-IRL/
• github: https://github.com/jangirrishabh/toyCarIRL

Reinforcement Learning and DQN, learning to play from pixels

• blog: https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html

Deep Learning in a Nutshell: Reinforcement Learning

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/

Write an AI to win at Pong from scratch with Reinforcement Learning

https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0#.n1pgn9chr

Learning Reinforcement Learning (with Code, Exercises and Solutions)

• blog: http://www.wildml.com/2016/10/learning-reinforcement-learning/
• github: https://github.com/dennybritz/reinforcement-learning

Deep Reinforcement Learning: Playing a Racing Game

https://lopespm.github.io/machine_learning/2016/10/06/deep-reinforcement-learning-racing-game.html

Experimenting with Reinforcement Learning and Active Inference

• blog: http://www.araya.org/archives/955
• github: https://github.com/arayabrain/BinarySearchLSTM

Deep reinforcement learning, battleship

• blog: http://efavdb.com/battleship/
• github: https://github.com/EFavDB/battleship

Deep Learning Research Review Week 2: Reinforcement Learning

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning

Reinforcement Learning: Artificial Intelligence in Game Playing

https://medium.com/@pavelkordik/reinforcement-learning-the-hardest-part-of-machine-learning-b667a22995ca#.jjiitflok

Artificial Intelligence’s Next Big Step: Reinforcement Learning

http://thenewstack.io/reinforcement-learning-ready-real-world/

Let’s make a DQN

Let’s make a DQN

• Theory: https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/
• Implementation: https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/
• Debugging: https://jaromiru.com/2016/10/12/lets-make-a-dqn-debugging/
• Full DQN: https://jaromiru.com/2016/10/21/lets-make-a-dqn-full-dqn/
• github: https://github.com/jaara/AI-blog/blob/master/CartPole-basic.py

Books

Reinforcement Learning: State-of-the-Art

• intro: “The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.”
• book: http://www.springer.com/gp/book/9783642276446#

Reinforcement Learning: An Introduction

• github: https://github.com/Mononofu/reinforcement-learning
• homepage: http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
• course: http://incompleteideas.net/rlai.cs.ualberta.ca/RLAI/RLAIcourse/2010.html
• book(1st edition): http://pan.baidu.com/s/1jkaMq
• book(2rd edition): http://pan.baidu.com/s/1dDnNEnR

Reinforcement Learning: An Introduction (Second edition, Draft)

• book: https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf
• mirror: https://pan.baidu.com/s/1slrMYkP
• github: https://github.com/ShangtongZhang/reinforcement-learning-an-introduction

The Self Learning Quant

• intro: explain and show the concept of self reinforcement learning combined with a neural network
• blog: https://medium.com/@danielzakrisson/the-self-learning-quant-d3329fcc9915#.9lsa5rh3e
• gihtub: https://github.com/danielzak/sl-quant

Reinforcement Learning: An Introduction

• author: Richard S. Sutton and Andrew G. Barto
• book: https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html
• solutions: https://github.com/btaba/intro-to-rl

Resources

Deep Reinforcement Learning Papers

https://github.com/junhyukoh/deep-reinforcement-learning-papers

Awesome Reinforcement Learning

• website: http://aikorea.org/awesome-rl/?utm_content=buffer5d0f3&utm_medium=social&utm_source=plus.google.com&utm_campaign=buffer#online-demos
• github: https://github.com/aikorea/awesome-rl

Deep Reinforcement Learning Papers

• github: https://github.com/muupan/deep-reinforcement-learning-papers

Deep Reinforcement Learning 深度增强学习资源

• blog: https://zhuanlan.zhihu.com/p/20885568

deep-reinforcement-learning-networks: A list of deep neural network architectures for reinforcement learning tasks

• github: https://github.com/5vision/deep-reinforcement-learning-networks

Deep Reinforcement Learning survey

• github: https://github.com/andrewliao11/Deep-Reinforcement-Learning-Survey

Studying Reinforcement Learning Guide

• github: https://github.com/0bserver07/Study-Reinforcement-Learning

Reading and Questions

What are the best books about reinforcement learning?

https://www.quora.com/What-are-the-best-books-about-reinforcement-learning

Tutorials

Making a Contextual Recommendation Engine

• intro: by Muktabh Mayank
• youtube: https://www.youtube.com/watch?v=ToTyNF9kXkk&hd=1http://weibo.com/1402400261/profile?topnav=1&wvr=6
• video: http://pan.baidu.com/s/1eQFFVns

Papers

Collaborative Deep Learning for Recommender Systems

• arxiv: https://arxiv.org/abs/1409.2944
• paper: http://www.wanghao.in/paper/KDD15_CDL.pdf

Image-based recommendations on styles and substitutes

• paper: http://cseweb.ucsd.edu/~jmcauley/pdfs/sigir15.pdf
• code: http://cseweb.ucsd.edu/~jmcauley/code/imageGraph.tar.gz
• data: http://jmcauley.ucsd.edu/data/amazon/

A Complex Network Approach for Collaborative Recommendation

• arxiv: http://arxiv.org/abs/1510.00585v1

Session-based Recommendations with Recurrent Neural Networks

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06939
• github: https://github.com/hidasib/GRU4Rec

Item2Vec: Neural Item Embedding for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1603.04259

Wide & Deep Learning for Recommender Systems

• intro: Google Research
• arxiv: http://arxiv.org/abs/1606.07792
• blog: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

Hybrid Recommender System based on Autoencoders

• arxiv: https://arxiv.org/abs/1606.07659
• github: https://github.com/fstrub95/Autoencoders_cf
• notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/06/hybrid-recommender-system-based-on-autoencoders.md

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

• paper: https://alexiskz.files.wordpress.com/2016/06/feature-rnn-paper1.pdf

Collaborative Filtering with Recurrent Neural Networks

• keywords: LSTM, movie recommendation
• arixv: http://arxiv.org/abs/1608.07400

Deep Neural Networks for YouTube Recommendations

• intro: RECSYS 2016. Google
• paper: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
• summary: https://blog.acolyer.org/2016/09/19/deep-neural-networks-for-youtube-recommendations/

Photo Filter Recommendation by Category-Aware Aesthetic Learning

• intro: Filter Aesthetic Comparison Dataset (FACD): 28,000 filtered images and 42,240 reliable image pairs with aesthetic comparison annotations
• arxiv: http://arxiv.org/abs/1608.05339

Convolutional Matrix Factorization for Document Context-Aware Recommendation

• project page: http://dm.postech.ac.kr/~cartopy/ConvMF/
• paper: http://dl.acm.org/citation.cfm?id=2959165

Deep learning for audio-based music recommendation

• slides: https://docs.google.com/presentation/d/1CRSAs2WOKo5mFhh5Iu-xkDfyJsg_NDL1r5dRtj6_aHo/edit#slide=id.p
• mirror: https://pan.baidu.com/s/1o8NaMPs

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

• arxiv: https://arxiv.org/abs/1609.02116

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00454

Recurrent Recommender Networks

• intro: University of Texas at Austin & Google Research & CMU & LinkedIn
• paper: http://alexbeutel.com/papers/rrn_wsdm2017.pdf

Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

• intro: Visnet. Flipkart’s visual search and recommendation system
• arxiv: https://arxiv.org/abs/1703.02344
• github: https://github.com/flipkart-incubator/fk-visual-search

What Your Image Reveals: Exploiting Visual Contents for Point-of-Interest Recommendation

• intro: Arizona State University & Michigan State University
• intro: Point-of-Interest (POI)
• paper: http://www.public.asu.edu/~swang187/publications/VPOI.pdf

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

• intro: Gravity R&D & Telefonica Research
• arxiv: https://arxiv.org/abs/1706.03847
• github: https://github.com/hidasib/GRU4Rec

On Sampling Strategies for Neural Network-based Collaborative Filtering

• intro: KDD 2017. University of California, Los Angeles & Yahoo! Research & Etsy Inc
• arxiv: https://arxiv.org/abs/1706.07881

Deep Learning based Recommender System: A Survey and New Perspectives

• intro: University of New South Wales & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1707.07435

Training Deep AutoEncoders for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1708.01715
• github: https://github.com/NVIDIA/DeepRecommender

Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback

• intro: Zhejiang University
• arxiv: https://arxiv.org/abs/1712.09043

Deep Reinforcement Learning for List-wise Recommendations

• intro: Michigan State University & Data Science Lab
• arxiv: https://arxiv.org/abs/1801.00209

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

• intro: Michigan State University & JD.com
• arxiv: https://arxiv.org/abs/1802.06501

Slides

Deep learning for music recommendation

• sldies: http://pan.baidu.com/s/1skriMJj

Deep learning for music recommendation and generation

• slides: https://docs.google.com/presentation/d/1AIotiiAp_528R90ll8j-Kc2EsRk2Oxc1poRgPXnEH8Y/edit
• mirror: https://pan.baidu.com/s/1czQQNO

Blogs

Recommending music on Spotify with deep learning

http://benanne.github.io/2014/08/05/spotify-cnns.html

Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE

http://blogs.aws.amazon.com/bigdata/post/TxGEL8IJ0CAXTK/Generating-Recommendations-at-Amazon-Scale-with-Apache-Spark-and-Amazon-DSSTNE

Recommending movies with deep learning

• blog: http://blog.richardweiss.org/2016/09/25/movie-embeddings.html
• ipn: https://github.com/ririw/ririw.github.io/blob/master/assets/Recommending%20movies.ipynb

Deep Learning Helps iHeartRadio Personalize Music Recommendations

• blog: https://news.developer.nvidia.com/deep-learning-helps-iheartradio-personalize-music-recommendations/

Applying deep learning to Related Pins

• intro: Pinterest
• blog: https://engineering.pinterest.com/blog/applying-deep-learning-related-pins

Recommendation System Algorithms: Main existing recommendation engines and how they work

https://blog.statsbot.co/recommendation-system-algorithms-ba67f39ac9a3

Building a Music Recommender with Deep Learning

• intro: Music recommender using deep learning with Keras and TensorFlow
• blog: http://mattmurray.net/building-a-music-recommender-with-deep-learning/
• github: https://github.com/mattmurray/music_recommender

Projects

NNRec: Neural models for Collaborative Filtering

• intro: Source code for, AutoRec, an autoencoder based model for collaborative filtering. This package also includes implementation of RBM based collaborative filtering model(RBM-CF).
• github: https://github.com/mesuvash/NNRec

Deep learning recommend system with TensorFlow

• intro: a general project to walk through the proceses of using TensorFlow
• github: https://github.com/tobegit3hub/deep_recommend_system

Deep Learning Recommender System

• github: https://github.com/freegraphics/MIDS

Keras Implementation of Recommender Systems

https://github.com/sonyisme/keras-recommendation

Videos

Deep Learning for Recommender Systems

• youtube: https://www.youtube.com/watch?v=KZ7bcfYGuxw

Using MXNet for Recommendation Modeling at Scale

• youtube: https://www.youtube.com/watch?v=cftJAuwKWkA
• mirror: https://pan.baidu.com/s/1kVsdrmR

Resources

Recommender Systems with Deep Learning

https://amundtveit.com/2016/11/20/recommender-systems-with-deep-learning/

Deep-Learning-for-Recommendation-Systems

• intro: This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
• github: https://github.com/robi56/Deep-Learning-for-Recommendation-Systems

Papers

Leaderboard

Papers

Papers

Tutorials

Practical Neural Networks for NLP

• intro: EMNLP 2016
• github: https://github.com/clab/dynet_tutorial_examples

Structured Neural Networks for NLP: From Idea to Code

• slides: https://github.com/neubig/yrsnlp-2016/blob/master/neubig16yrsnlp.pdf
• github: https://github.com/neubig/yrsnlp-2016

Understanding Deep Learning Models in NLP

http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

Deep learning for natural language processing, Part 1

https://softwaremill.com/deep-learning-for-nlp/

Neural Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

• intro: NIPS 2014 deep learning workshop
• arxiv: http://arxiv.org/abs/1411.2539
• github: https://github.com/ryankiros/visual-semantic-embedding
• results: http://www.cs.toronto.edu/~rkiros/lstm_scnlm.html
• demo: http://deeplearning.cs.toronto.edu/i2t

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

• arxiv: http://arxiv.org/abs/1503.00075
• github: https://github.com/stanfordnlp/treelstm
• github(Theano): https://github.com/ofirnachum/tree_rnn

Visualizing and Understanding Neural Models in NLP

• arxiv: http://arxiv.org/abs/1506.01066
• github: https://github.com/jiweil/Visualizing-and-Understanding-Neural-Models-in-NLP

Character-Aware Neural Language Models

• paper: http://arxiv.org/abs/1508.06615
• github: https://github.com/yoonkim/lstm-char-cnn

Skip-Thought Vectors

• paper: http://arxiv.org/abs/1506.06726
• github: https://github.com/ryankiros/skip-thoughts

A Primer on Neural Network Models for Natural Language Processing

• arxiv: http://arxiv.org/abs/1510.00726

Character-aware Neural Language Models

• arxiv: http://arxiv.org/abs/1508.06615

Neural Variational Inference for Text Processing

• arxiv: http://arxiv.org/abs/1511.06038
• notes: http://dustintran.com/blog/neural-variational-inference-for-text-processing/
• github: https://github.com/carpedm20/variational-text-tensorflow
• github: https://github.com/cheng6076/NVDM

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1510.09202

MUSIO: A Deep Learning based Chatbot Getting Smarter

• homepage: http://ec2-204-236-149-143.us-west-1.compute.amazonaws.com:9000/
• github(Torch7): https://github.com/deepcoord/seq2seq

Translation

Learning phrase representations using rnn encoder-decoder for statistical machine translation

• intro: GRU. EMNLP 2014
• arxiv: http://arxiv.org/abs/1406.1078

Neural Machine Translation by Jointly Learning to Align and Translate

• intro: ICLR 2015
• arxiv: http://arxiv.org/abs/1409.0473
• github: https://github.com/lisa-groundhog/GroundHog

Multi-Source Neural Translation

• intro: “report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.”
• arxiv: Multi-Source Neural Translation
• github(Zoph_RNN): https://github.com/isi-nlp/Zoph_RNN
• video: http://research.microsoft.com/apps/video/default.aspx?id=260336

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

• arxiv: http://arxiv.org/abs/1601.01073
• github: https://github.com/nyu-dl/dl4mt-multi
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/multi-way-nmt-shared-attention.md

Modeling Coverage for Neural Machine Translation

• arxiv: http://arxiv.org/abs/1601.04811

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

• arxiv: http://arxiv.org/abs/1603.06147
• github: https://github.com/nyu-dl/dl4mt-cdec

NEMATUS: Attention-based encoder-decoder model for neural machine translation

• github: https://github.com/rsennrich/nematus

Variational Neural Machine Translation

• intro: EMNLP 2016
• arxiv: https://arxiv.org/abs/1605.07869
• github: https://github.com/DeepLearnXMU/VNMT

Neural Network Translation Models for Grammatical Error Correction

• arxiv: http://arxiv.org/abs/1606.00189

Linguistic Input Features Improve Neural Machine Translation

• arxiv: http://arxiv.org/abs/1606.02892
• github: https://github.com/rsennrich/nematus

Sequence-Level Knowledge Distillation

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1606.07947
• github: https://github.com/harvardnlp/nmt-android

Neural Machine Translation: Breaking the Performance Plateau

• slides: http://www.meta-net.eu/events/meta-forum-2016/slides/09_sennrich.pdf

Tips on Building Neural Machine Translation Systems

• github: https://github.com/neubig/nmt-tips

Semi-Supervised Learning for Neural Machine Translation

• intro: ACL 2016. Tsinghua University & Baidu Inc
• arxiv: http://arxiv.org/abs/1606.04596

EUREKA-MangoNMT: A C++ toolkit for neural machine translation for CPU

• github: https://github.com/jiajunzhangnlp/EUREKA-MangoNMT

Deep Character-Level Neural Machine Translation

• github: https://github.com/SwordYork/DCNMT

Neural Machine Translation Implementations

• github: https://github.com/jonsafari/nmt-list

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

• arxiv: http://arxiv.org/abs/1609.08144v1

Learning to Translate in Real-time with Neural Machine Translation

• arxiv: https://arxiv.org/abs/1610.00388

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

• arxiv: https://arxiv.org/abs/1610.01108
• github: https://github.com/emjotde/amunmt

Fully Character-Level Neural Machine Translation without Explicit Segmentation

• arxiv: https://arxiv.org/abs/1610.03017
• github: https://github.com/nyu-dl/dl4mt-c2c

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

• arxiv: https://arxiv.org/abs/1610.03164

Neural Machine Translation in Linear Time

• intro: ByteNet
• arxiv: https://arxiv.org/abs/1610.10099
• github: https://github.com/paarthneekhara/byteNet-tensorflow
• github(Tensorflow): https://github.com/buriburisuri/ByteNet

Neural Machine Translation with Reconstruction

• arxiv: https://arxiv.org/abs/1611.01874

A Convolutional Encoder Model for Neural Machine Translation

• intro: ACL 2017. Facebook AI Research
• arxiv: https://arxiv.org/abs/1611.02344
• github: https://github.com//pravarmahajan/cnn-encoder-nmt

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

• arxiv: https://arxiv.org/abs/1611.04798

MXNMT: MXNet based Neural Machine Translation

• github: https://github.com/magic282/MXNMT

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

• intro: Dublin City University & Trinity College Dublin
• arxiv: https://arxiv.org/abs/1702.01287

Massive Exploration of Neural Machine Translation Architectures

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1703.03906
• github: https://github.com/google/seq2seq/

Depthwise Separable Convolutions for Neural Machine Translation

• intro: Google Brain & University of Toronto
• arxiv: https://arxiv.org/abs/1706.03059

Deep Architectures for Neural Machine Translation

• intro: WMT 2017 research track. University of Edinburgh & Charles University
• arxiv: https://arxiv.org/abs/1707.07631
• github: https://github.com/Avmb/deep-nmt-architectures

Marian: Fast Neural Machine Translation in C++

• intro: Microsoft & Adam Mickiewicz University in Poznan & University of Edinburgh
• homepage: https://marian-nmt.github.io/
• arxiv: https://arxiv.org/abs/1804.00344
• github: https://github.com/marian-nmt/marian

Summarization

Extraction of Salient Sentences from Labelled Documents

• arxiv: http://arxiv.org/abs/1412.6815
• github: https://github.com/mdenil/txtnets
• notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2014/06/model-visualizing-summarising-conv-net.md

A Neural Attention Model for Abstractive Sentence Summarization

• intro: EMNLP 2015. Facebook AI Research
• arxiv: http://arxiv.org/abs/1509.00685
• github: https://github.com/facebook/NAMAS
• github(TensorFlow): https://github.com/carpedm20/neural-summary-tensorflow

A Convolutional Attention Network for Extreme Summarization of Source Code

• homepage: http://groups.inf.ed.ac.uk/cup/codeattention/
• arxiv: http://arxiv.org/abs/1602.03001
• github: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/02/conv-attention-network-source-code-summarization.md

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

• intro: BM Watson & Université de Montréal
• arxiv: http://arxiv.org/abs/1602.06023

textsum: Text summarization with TensorFlow

• blog: https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
• github: https://github.com/tensorflow/models/tree/master/textsum

How to Run Text Summarization with TensorFlow

• blog: https://medium.com/@surmenok/how-to-run-text-summarization-with-tensorflow-d4472587602d#.mll1rqgjg
• github: https://github.com/surmenok/TextSum

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

• intro: ACL 2016
• arxiv: https://arxiv.org/abs/1603.01547
• github: https://github.com/rkadlec/asreader

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

• arxiv: http://arxiv.org/abs/1606.02858
• github: https://github.com/danqi/rc-cnn-dailymail

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.02250
• dataset(“HFL-RC”): http://hfl.iflytek.com/chinese-rc/

Separating Answers from Queries for Neural Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.03316
• github: https://github.com/dirkweissenborn/qa_network

Attention-over-Attention Neural Networks for Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.04423
• github: https://github.com/OlavHN/attention-over-attention

Teaching Machines to Read and Comprehend CNN News and Children Books using Torch

• github: https://github.com/ganeshjawahar/torch-teacher

Reasoning with Memory Augmented Neural Networks for Language Comprehension

• arxiv: https://arxiv.org/abs/1610.06454

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

• project page: https://allenai.github.io/bi-att-flow/
• github: https://github.com/allenai/bi-att-flow

NewsQA: A Machine Comprehension Dataset

• arxiv: https://arxiv.org/abs/1611.09830
• dataset: http://datasets.maluuba.com/NewsQA
• github: https://github.com/Maluuba/newsqa

Gated-Attention Readers for Text Comprehension

• intro: CMU
• arxiv: https://arxiv.org/abs/1606.01549
• github: https://github.com/bdhingra/ga-reader

Get To The Point: Summarization with Pointer-Generator Networks

• intro: ACL 2017. Stanford University & Google Brain
• arxiv: https://arxiv.org/abs/1704.04368
• github: https://github.com/abisee/pointer-generator

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

• arxiv: http://arxiv.org/abs/1506.00195
• github: https://github.com/npow/RNN-EM

Neural Semantic Encoders

• intro: EACL 2017
• arxiv: https://arxiv.org/abs/1607.04315
• github(Keras): https://github.com/pdasigi/neural-semantic-encoders

Neural Tree Indexers for Text Understanding

• arxiv: https://arxiv.org/abs/1607.04492
• bitbucket: https://bitbucket.org/tsendeemts/nti/src

Better Text Understanding Through Image-To-Text Transfer

• intro: Google Brain & Technische Universität München
• arxiv: https://arxiv.org/abs/1705.08386

Text Classification

Convolutional Neural Networks for Sentence Classification

• intro: EMNLP 2014
• arxiv: http://arxiv.org/abs/1408.5882
• github(Theano): https://github.com/yoonkim/CNN_sentence
• github(Torch): https://github.com/harvardnlp/sent-conv-torch
• github(Keras): https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras
• github(Tensorflow): https://github.com/abhaikollara/CNN-Sentence-Classification

Recurrent Convolutional Neural Networks for Text Classification

• paper: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745/9552
• github: https://github.com/knok/rcnn-text-classification

Character-level Convolutional Networks for Text Classification

• intro: NIPS 2015. “Text Understanding from Scratch”
• arxiv: http://arxiv.org/abs/1509.01626
• github: https://github.com/zhangxiangxiao/Crepe
• datasets: http://goo.gl/JyCnZq
• github(TensorFlow): https://github.com/mhjabreel/CharCNN

A C-LSTM Neural Network for Text Classification

• arxiv: http://arxiv.org/abs/1511.08630

Rationale-Augmented Convolutional Neural Networks for Text Classification

• arxiv: http://arxiv.org/abs/1605.04469

Text classification using DIGITS and Torch7

• github: https://github.com/NVIDIA/DIGITS/tree/master/examples/text-classification

Recurrent Neural Network for Text Classification with Multi-Task Learning

• arxiv: http://arxiv.org/abs/1605.05101

Deep Multi-Task Learning with Shared Memory

• intro: EMNLP 2016
• arxiv: https://arxiv.org/abs/1609.07222

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

• arxiv: http://arxiv.org/abs/1605.07725
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md

Sentence Convolution Code in Torch: Text classification using a convolutional neural network

• github: https://github.com/harvardnlp/sent-conv-torch

Bag of Tricks for Efficient Text Classification

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1607.01759
• github: https://github.com/kemaswill/fasttext_torch
• github: https://github.com/facebookresearch/fastText

Actionable and Political Text Classification using Word Embeddings and LSTM

• arxiv: http://arxiv.org/abs/1607.02501

Implementing a CNN for Text Classification in TensorFlow

• blog: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/

fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification

• github: https://github.com/textclf/fancy-cnn

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

• arxiv: http://arxiv.org/abs/1609.00718

Tweet Classification using RNN and CNN

• github: https://github.com/ganeshjawahar/tweet-classify

Hierarchical Attention Networks for Document Classification

• intro: CMU & MSR. NAACL 2016
• paper: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf
• github(TensorFlow): https://github.com/raviqqe/tensorflow-font2char2word2sent2doc
• github(TensorFlow): https://github.com/ematvey/deep-text-classifier

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

• arxiv: https://arxiv.org/abs/1611.01884
• github(MXNet): https://github.com/Ldpe2G/AC-BLSTM

Generative and Discriminative Text Classification with Recurrent Neural Networks

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1703.01898

Adversarial Multi-task Learning for Text Classification

• intro: ACL 2017
• arxiv: https://arxiv.org/abs/1704.05742
• data: http://nlp.fudan.edu.cn/data/

Deep Text Classification Can be Fooled

• intro: Renmin University of China
• arxiv: https://arxiv.org/abs/1704.08006

Deep neural network framework for multi-label text classification

• github: https://github.com/inspirehep/magpie

Multi-Task Label Embedding for Text Classification

• intro: Shanghai Jiao Tong University
• arxiv: https://arxiv.org/abs/1710.07210

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

• intro: Chinese Academy of Sciences. accepted for publication in Neural Networks
• arxiv: https://arxiv.org/abs/1701.00185
• github: https://github.com/jacoxu/STC2

Alignment

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

• arxiv: http://arxiv.org/abs/1506.06724
• github: https://github.com/ryankiros/neural-storyteller

Dialog

Visual Dialog

• webiste: http://visualdialog.org/
• arxiv: https://arxiv.org/abs/1611.08669
• github: https://github.com/batra-mlp-lab/visdial-amt-chat
• github(Torch): https://github.com/batra-mlp-lab/visdial
• github(PyTorch): https://github.com/Cloud-CV/visual-chatbot
• demo: http://visualchatbot.cloudcv.org/

Papers, code and data from FAIR for various memory-augmented nets with application to text understanding and dialogue.

• post: https://www.facebook.com/yann.lecun/posts/10154070851697143

Neural Emoji Recommendation in Dialogue Systems

• intro: Tsinghua University & Baidu
• arxiv: https://arxiv.org/abs/1612.04609

Memory Networks

Neural Turing Machines

• paper: http://arxiv.org/abs/1410.5401
• Chs: http://www.jianshu.com/p/94dabe29a43b
• github: https://github.com/shawntan/neural-turing-machines
• github: https://github.com/DoctorTeeth/diffmem
• github: https://github.com/carpedm20/NTM-tensorflow
• blog: https://blog.aidangomez.ca/2016/05/16/The-Neural-Turing-Machine/

Memory Networks

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1410.3916
• github: https://github.com/npow/MemNN

End-To-End Memory Networks

• intro: Facebook AI Research
• intro: Continuous version of memory extraction via softmax. “Weakly supervised memory networks”
• arxiv: http://arxiv.org/abs/1503.08895
• github: https://github.com/facebook/MemNN
• github: https://github.com/vinhkhuc/MemN2N-babi-python
• github: https://github.com/npow/MemN2N
• github: https://github.com/domluna/memn2n
• github(Tensorflow): https://github.com/abhaikollara/MemN2N-Tensorflow
• video: http://research.microsoft.com/apps/video/default.aspx?id=259920&r=1
• video: http://pan.baidu.com/s/1pKiGLzP

Reinforcement Learning Neural Turing Machines - Revised

• arxiv: http://arxiv.org/abs/1505.00521
• github: https://github.com/ilyasu123/rlntm

Learning to Transduce with Unbounded Memory

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1506.02516

How to Code and Understand DeepMind’s Neural Stack Machine

• blog: https://iamtrask.github.io/2016/02/25/deepminds-neural-stack-machine/
• video tutorial: http://pan.baidu.com/s/1qX0EGDe

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

• intro: Memory networks implemented via rnns and gated recurrent units (GRUs).
• arxiv: http://arxiv.org/abs/1506.07285
• blog(“Implementing Dynamic memory networks”): http://yerevann.github.io//2016/02/05/implementing-dynamic-memory-networks/
• github(Python): https://github.com/swstarlab/DynamicMemoryNetworks

Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

• intro: extensions for the Dynamic Memory Network (DMN)
• arxiv: https://arxiv.org/abs/1703.03939
• github: https://github.com/rgsachin/DMTN

Structured Memory for Neural Turing Machines

• intro: IBM Watson
• arxiv: http://arxiv.org/abs/1510.03931

Dynamic Memory Networks for Visual and Textual Question Answering

• intro: MetaMind 2016
• arxiv: http://arxiv.org/abs/1603.01417
• slides: http://slides.com/smerity/dmn-for-tqa-and-vqa-nvidia-gtc#/
• github: https://github.com/therne/dmn-tensorflow
• github(Theano): https://github.com/ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus
• review: https://www.technologyreview.com/s/600958/the-memory-trick-making-computers-seem-smarter/
• github(Tensorflow): https://github.com/DeepRNN/visual_question_answering

Neural GPUs Learn Algorithms

• arxiv: http://arxiv.org/abs/1511.08228
• github: https://github.com/tensorflow/models/tree/master/neural_gpu
• github: https://github.com/ikostrikov/torch-neural-gpu
• github: https://github.com/tristandeleu/neural-gpu

Hierarchical Memory Networks

• arxiv: http://arxiv.org/abs/1605.07427

Convolutional Residual Memory Networks

• arxiv: http://arxiv.org/abs/1606.05262

NTM-Lasagne: A Library for Neural Turing Machines in Lasagne

• github: https://github.com/snipsco/ntm-lasagne
• blog: https://medium.com/snips-ai/ntm-lasagne-a-library-for-neural-turing-machines-in-lasagne-2cdce6837315#.96pnh1m6j

Evolving Neural Turing Machines for Reward-based Learning

• homepage: http://sebastianrisi.com/entm/
• paper: http://sebastianrisi.com/wp-content/uploads/greve_gecco16.pdf
• code: https://www.dropbox.com/s/t019mwabw5nsnxf/neuralturingmachines-master.zip?dl=0

Hierarchical Memory Networks for Answer Selection on Unknown Words

• intro: COLING 2016
• arxiv: https://arxiv.org/abs/1609.08843
• github: https://github.com/jacoxu/HMN4QA

Gated End-to-End Memory Networks

• arxiv: https://arxiv.org/abs/1610.04211

Can Active Memory Replace Attention?

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1610.08613

Papers

Globally Normalized Transition-Based Neural Networks

• intro: speech tagging, dependency parsing and sentence compression
• arxiv: http://arxiv.org/abs/1603.06042
• github(SyntaxNet): https://github.com/tensorflow/models/tree/master/syntaxnet

A Decomposable Attention Model for Natural Language Inference

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1606.01933
• github(Keras+spaCy): https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment

Improving Recurrent Neural Networks For Sequence Labelling

• arxiv: http://arxiv.org/abs/1606.02555

Recurrent Memory Networks for Language Modeling

• arixv: http://arxiv.org/abs/1601.01272
• github: https://github.com/ketranm/RMN

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

• intro: MIT Media Lab
• arixv: http://arxiv.org/abs/1607.07514

Learning text representation using recurrent convolutional neural network with highway layers

• intro: Neu-IR ‘16 SIGIR Workshop on Neural Information Retrieval
• arxiv: http://arxiv.org/abs/1606.06905
• github: https://github.com/wenying45/deep_learning_tutorial/tree/master/rcnn-hw

Ask the GRU: Multi-task Learning for Deep Text Recommendations

• arxiv: http://arxiv.org/abs/1609.02116

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

• intro: COLING 2016
• arxiv: https://arxiv.org/abs/1610.03342

Visualizing Linguistic Shift

• arxiv: https://arxiv.org/abs/1611.06478

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

• intro: The University of Tokyo & Salesforce Research
• arxiv: https://arxiv.org/abs/1611.01587

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

• intro: Google Brain & Google Research & University of Toronto
• intro: Just attention + positional encoding = state of the art
• arxiv: https://arxiv.org/abs/1706.03762
• github(Chainer): https://github.com/soskek/attention_is_all_you_need

Recent Trends in Deep Learning Based Natural Language Processing

• intro: Beijing Institute of Technology & National University of Singapore & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1708.02709

HotFlip: White-Box Adversarial Examples for NLP

• intro: University of Oregon & Nanjing University
• arxiv: https://arxiv.org/abs/1712.06751

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

• intro: ACL 2018
• arxiv: https://arxiv.org/abs/1804.09160

Interesting Applications

Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

• arxiv: http://arxiv.org/abs/1606.05611

sk_p: a neural program corrector for MOOCs

• intro: MIT
• intro: Using seq2seq to fix buggy code submissions in MOOCs
• arxiv: http://arxiv.org/abs/1607.02902

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

• intro: EMNLP 2016
• intro: translating natural language queries into regular expressions which embody their meaning
• arxiv: http://arxiv.org/abs/1608.03000

emoji2vec: Learning Emoji Representations from their Description

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1609.08359

Inside-Outside and Forward-Backward Algorithms Are Just Backprop (Tutorial Paper)

• paper: https://www.cs.jhu.edu/~jason/papers/eisner.spnlp16.pdf

Cruciform: Solving Crosswords with Natural Language Processing

• arxiv: https://arxiv.org/abs/1611.02360

Smart Reply: Automated Response Suggestion for Email

• intro: Google. KDD 2016
• arxiv: https://arxiv.org/abs/1606.04870
• notes: https://blog.acolyer.org/2016/11/24/smart-reply-automated-response-suggestion-for-email/

Deep Learning for RegEx

• intro: a winning submission of Extraction of product attribute values competition (CrowdAnalytix)
• blog: http://dlacombejr.github.io/2016/11/13/deep-learning-for-regex.html

Learning Python Code Suggestion with a Sparse Pointer Network

• intro: Learning to Auto-Complete using RNN Language Models
• intro: University College London
• arxiv: https://arxiv.org/abs/1611.08307
• github: https://github.com/uclmr/pycodesuggest

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

https://arxiv.org/abs/1703.02458

Convolutional Sequence to Sequence Learning

• arxiv: https://arxiv.org/abs/1705.03122
• paper: https://s3.amazonaws.com/fairseq/papers/convolutional-sequence-to-sequence-learning.pdf
• github: https://github.com/facebookresearch/fairseq

DeepFix: Fixing Common C Language Errors by Deep Learning

• intro: AAAI 2017. Indian Institute of Science
• project page: http://www.iisc-seal.net/deepfix
• paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14603/13921
• bitbucket: https://bitbucket.org/iiscseal/deepfix

Hierarchically-Attentive RNN for Album Summarization and Storytelling

• intro: EMNLP 2017. UNC Chapel Hill
• arxiv: https://arxiv.org/abs/1708.02977

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

• arxiv: http://arxiv.org/abs/1605.00942
• github: https://github.com/senarvi/theanolm

NLP-Caffe: natural language processing with Caffe

• github: https://github.com/Russell91/nlpcaffe

DL4NLP: Deep Learning for Natural Language Processing

• github: https://github.com/nokuno/dl4nlp

Combining CNN and RNN for spoken language identification

• blog: http://yerevann.github.io//2016/06/26/combining-cnn-and-rnn-for-spoken-language-identification/
• github: https://github.com/YerevaNN/Spoken-language-identification/tree/master/theano

Character-Aware Neural Language Models: LSTM language model with CNN over characters in TensorFlow

• github: https://github.com/carpedm20/lstm-char-cnn-tensorflow

Neural Relation Extraction with Selective Attention over Instances

• paper: http://nlp.csai.tsinghua.edu.cn/~lzy/publications/acl2016_nre.pdf
• github: https://github.com/thunlp/NRE

deep-simplification: Text simplification using RNNs

• intro: achieves a BLEU score of 61.14
• github: https://github.com/mbartoli/deep-simplification

lamtram: A toolkit for language and translation modeling using neural networks

• github: https://github.com/neubig/lamtram

Lango: Language Lego

• intro: Lango is a natural language processing library for working with the building blocks of language.
• github: https://github.com/ayoungprogrammer/Lango

Sequence-to-Sequence Learning with Attentional Neural Networks

• github(Torch): https://github.com/harvardnlp/seq2seq-attn

harvardnlp code

• intro: pen-source implementations of popular deep learning techniques with applications to NLP
• homepage: http://nlp.seas.harvard.edu/code/

Seq2seq: Sequence to Sequence Learning with Keras

• github: https://github.com/farizrahman4u/seq2seq

debug seq2seq

• github: https://github.com/nicolas-ivanov/debug_seq2seq

Recurrent & convolutional neural network modules

• intro: This repo contains Theano implementations of popular neural network components and optimization methods.
• github: https://github.com/taolei87/rcnn

Datasets

Datasets for Natural Language Processing

• github: https://github.com/karthikncode/nlp-datasets

Blogs

How to read: Character level deep learning

• blog: https://offbit.github.io/how-to-read/
• github: https://github.com/offbit/char-models

Heavy Metal and Natural Language Processing

• part 1: http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/

Sequence To Sequence Attention Models In PyCNN

https://talbaumel.github.io/Neural+Attention+Mechanism.html

Source Code Classification Using Deep Learning

http://blog.aylien.com/source-code-classification-using-deep-learning/

My Process for Learning Natural Language Processing with Deep Learning

https://medium.com/@MichaelTeifel/my-process-for-learning-natural-language-processing-with-deep-learning-bd0a64a36086

Convolutional Methods for Text

https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

Word2Vec

Word2Vec Tutorial - The Skip-Gram Model

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

Word2Vec Tutorial Part 2 - Negative Sampling

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

Word2Vec Resources

http://mccormickml.com/2016/04/27/word2vec-resources/

Demos

AskImage.org - Deep Learning for Answering Questions about Images

• homepage: http://www.askimage.org/

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

• youtube: https://www.youtube.com/watch?v=7s-erJbCkaY

Resources

So, you need to understand language data? Open-source NLP software can help!

• blog: http://entopix.com/so-you-need-to-understand-language-data-open-source-nlp-software-can-help.html

Curated list of resources on building bots

• github: https://github.com/hackerkid/bots

Notes for deep learning on NLP

https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7