Training Deep Neural Networks


Published: 09 Oct 2015



Learning A Deep Compact Image Representation for Visual Tracking

Hierarchical Convolutional Features for Visual Tracking

Robust Visual Tracking via Convolutional Networks


Transferring Rich Feature Hierarchies for Robust Visual Tracking


Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

RATM: Recurrent Attentive Tracking Model

Understanding and Diagnosing Visual Tracking Systems

Recurrently Target-Attending Tracking

Visual Tracking with Fully Convolutional Networks

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

Learning to Track at 100 FPS with Deep Regression Networks

Learning by tracking: Siamese CNN for robust target association

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Fully-Convolutional Siamese Networks for Object Tracking

Hedged Deep Tracking


Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

Visual Tracking via Shallow and Deep Collaborative Model

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Predictive Vision Model (PVM)

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

Multi-Person Tracking by Multicut and Deep Matching

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

Multi-Class Multi-Object Tracking using Changing Point Detection

Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

OTB Results: visual tracker benchmark results

Convolutional Regression for Visual Tracking

Semantic tracking: Single-target tracking with inter-supervised convolutional networks

SANet: Structure-Aware Network for Visual Tracking

ECO: Efficient Convolution Operators for Tracking

Dual Deep Network for Visual Tracking

Deep Motion Features for Visual Tracking

Globally Optimal Object Tracking with Fully Convolutional Networks

Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation

Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

Large Margin Object Tracking with Circulant Feature Maps

Simple Online and Realtime Tracking with a Deep Association Metric

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

End-to-end representation learning for Correlation Filter based tracking

Context-Aware Correlation Filter Tracking

Robust Multi-view Pedestrian Tracking Using Neural Networks

Re3 : Real-Time Recurrent Regression Networks for Object Tracking

Robust Tracking Using Region Proposal Networks

Deep Network Flow for Multi-Object Tracking

Hierarchical Attentive Recurrent Tracking

Siamese Learning Visual Tracking: A Survey

Robust Visual Tracking via Hierarchical Convolutional Features

CREST: Convolutional Residual Learning for Visual Tracking

Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism

Learning Policies for Adaptive Tracking with Deep Feature Cascades

Recurrent Filter Learning for Visual Tracking

Semantic Texture for Robust Dense Tracking

Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects

Tracking with Reinforcement Learning

Deep Reinforcement Learning for Visual Object Tracking in Videos

Visual Tracking by Reinforced Decision Making

End-to-end Active Object Tracking via Reinforcement Learning

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

Detect to Track and Track to Detect



Published: 09 Oct 2015



Published: 09 Oct 2015


Types of RNN

1) Plain Tanh Recurrent Nerual Networks

2) Gated Recurrent Neural Networks (GRU)

3) Long Short-Term Memory (LSTM)


The Unreasonable Effectiveness of Recurrent Neural Networks

Understanding LSTM Networks

A Beginner’s Guide to Recurrent Networks and LSTMs

A Deep Dive into Recurrent Neural Nets

Exploring LSTMs

A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach

Long Short-Term Memory: Tutorial on LSTM Recurrent Networks

LSTM implementation explained

Recurrent Neural Networks Tutorial

Recurrent Neural Networks in DL4J

Learning RNN Hierarchies

Element-Research Torch RNN Tutorial for recurrent neural nets : let’s predict time series with a laptop GPU

RNNs in Tensorflow, a Practical Guide and Undocumented Features

Learning about LSTMs using Torch

Build a Neural Network (LIVE)

Deriving LSTM Gradient for Backpropagation

TensorFlow RNN Tutorial

RNN Training Tips and Tricks

Tips for Training Recurrent Neural Networks

A Tour of Recurrent Neural Network Algorithms for Deep Learning

How to build a Recurrent Neural Network in TensorFlow

How to build a Recurrent Neural Network in TensorFlow (1/7)

Using the RNN API in TensorFlow (2/7)

Using the LSTM API in TensorFlow (3/7)

Using the Multilayered LSTM API in TensorFlow (4/7)

Using the DynamicRNN API in TensorFlow (5/7)

Using the Dropout API in TensorFlow (6/7)

Unfolding RNNs

Unfolding RNNs: RNN : Concepts and Architectures

Unfolding RNNs II: Vanilla, GRU, LSTM RNNs from scratch in Tensorflow

Train RNN

On the difficulty of training Recurrent Neural Networks

A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Batch Normalized Recurrent Neural Networks

Sequence Level Training with Recurrent Neural Networks

Training Recurrent Neural Networks (PhD thesis)

Deep learning for control using augmented Hessian-free optimization

Hierarchical Conflict Propagation: Sequence Learning in a Recurrent Deep Neural Network

Recurrent Batch Normalization

Batch normalized LSTM for Tensorflow

Optimizing Performance of Recurrent Neural Networks on GPUs

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

Explaining and illustrating orthogonal initialization for recurrent neural networks

Professor Forcing: A New Algorithm for Training Recurrent Networks

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

Tuning Recurrent Neural Networks with Reinforcement Learning (RL Tuner)

Capacity and Trainability in Recurrent Neural Networks

Learn To Execute Programs

Learning to Execute

Neural Programmer-Interpreters

A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

Neural Random-Access Machines

Attention Models

Recurrent Models of Visual Attention

Recurrent Model of Visual Attention

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

A Neural Attention Model for Abstractive Sentence Summarization

Effective Approaches to Attention-based Neural Machine Translation

Generating Images from Captions with Attention

Attention and Memory in Deep Learning and NLP

Survey on the attention based RNN model and its applications in computer vision

Attention in Long Short-Term Memory Recurrent Neural Networks

How to Visualize Your Recurrent Neural Network with Attention in Keras


Generating Sequences With Recurrent Neural Networks

A Clockwork RNN

Unsupervised Learning of Video Representations using LSTMs

An Empirical Exploration of Recurrent Network Architectures

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

LSTM: A Search Space Odyssey

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

A Critical Review of Recurrent Neural Networks for Sequence Learning

Visualizing and Understanding Recurrent Networks

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Grid Long Short-Term Memory

Depth-Gated LSTM

Deep Knowledge Tracing

Top-down Tree Long Short-Term Memory Networks

Improving performance of recurrent neural network with relu nonlinearity

Alternative structures for character-level RNNs

Long Short-Term Memory-Networks for Machine Reading

Lipreading with Long Short-Term Memory

Associative Long Short-Term Memory

Representation of linguistic form and function in recurrent neural networks

Architectural Complexity Measures of Recurrent Neural Networks

Easy-First Dependency Parsing with Hierarchical Tree LSTMs

Training Input-Output Recurrent Neural Networks through Spectral Methods

Sequential Neural Models with Stochastic Layers

Neural networks with differentiable structure

What You Get Is What You See: A Visual Markup Decompiler

Hybrid computing using a neural network with dynamic external memory

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks


Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

Recurrent Memory Array Structures

Recurrent Highway Networks

DeepSoft: A vision for a deep model of software

Recurrent Neural Networks With Limited Numerical Precision

Hierarchical Multiscale Recurrent Neural Networks


LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Full-Capacity Unitary Recurrent Neural Networks

DeepCoder: Learning to Write Programs

shuttleNet: A biologically-inspired RNN with loop connection and parameter sharing

Tracking the World State with Recurrent Entity Networks

Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

The Statistical Recurrent Unit

Factorization tricks for LSTM networks

Bayesian Recurrent Neural Networks

Fast-Slow Recurrent Neural Networks

Visualizing LSTM decisions

Recurrent Additive Networks


NeuralTalk (Deprecated): a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences

NeuralTalk2: Efficient Image Captioning code in Torch, runs on GPU

char-rnn in Blocks

Project: pycaffe-recurrent

Using neural networks for password cracking

torch-rnn: Efficient, reusable RNNs and LSTMs for torch

Deploying a model trained with GPU in Torch into JavaScript, for everyone to use

LSTM implementation on Caffe

JNN: Java Neural Network Library

LSTM-Autoencoder: Seq2Seq LSTM Autoencoder

RNN Language Model Variations

keras-extra: Extra Layers for Keras to connect CNN with RNN

Dynamic Vanilla RNN, GRU, LSTM,2layer Stacked LSTM with Tensorflow Higher Order Ops

PRNN: A fast implementation of recurrent neural network layers in CUDA

min-char-rnn: Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

rnn: Recurrent Neural Network library for Torch7’s nn

word-rnn-tensorflow: Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow

tf-char-rnn: Tensorflow implementation of char-rnn

translit-rnn: Automatic transliteration with LSTM Simple implementation of LSTM in Tensorflow in 50 lines (+ 130 lines of data generation and comments)

Handwriting generating with RNN

RecNet - Recurrent Neural Network Framework


Survey on Attention-based Models Applied in NLP

Survey on Advanced Attention-based Models

Online Representation Learning in Recurrent Neural Language Models

Fun with Recurrent Neural Nets: One More Dive into CNTK and TensorFlow

Materials to understand LSTM

Understanding LSTM and its diagrams


Persistent RNNs: 30 times faster RNN layers at small mini-batch sizes

Persistent RNNs: Stashing Recurrent Weights On-Chip

All of Recurrent Neural Networks

Rolling and Unrolling RNNs

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: LSTM regression using TensorFlow


Machines and Magic: Teaching Computers to Write Harry Potter

Crash Course in Recurrent Neural Networks for Deep Learning

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

Recurrent Neural Networks in Tensorflow

Written Memories: Understanding, Deriving and Extending the LSTM

Attention and Augmented Recurrent Neural Networks

Interpreting and Visualizing Neural Networks for Text Processing

A simple design pattern for recurrent deep learning in TensorFlow

RNN Spelling Correction: To crack a nut with a sledgehammer

Recurrent Neural Network Gradients, and Lessons Learned Therein

A noob’s guide to implementing RNN-LSTM using Tensorflow

Non-Zero Initial States for Recurrent Neural Networks

Interpreting neurons in an LSTM network

Optimizing RNN (Baidu Silicon Valley AI Lab)

Optimizing RNN performance

Optimizing RNNs with Differentiable Graphs


Awesome Recurrent Neural Networks - A curated list of resources dedicated to RNN

Jürgen Schmidhuber’s page on Recurrent Neural Networks

Reading and Questions

Are there any Recurrent convolutional neural network network implementations out there ?

Published: 09 Oct 2015

Image Retrieval


Published: 09 Oct 2015

Reinforcement Learning


Demystifying Deep Reinforcement Learning (Part1)

Deep Reinforcement Learning With Neon (Part2)

Deep Reinforcement Learning

Deep Reinforcement Learning

Deep Reinforcement Learning: Pong from Pixels

Deep Reinforcement Learning

Deep Reinforcement Learning

The Nuts and Bolts of Deep RL Research

Reinforcement learning explained

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

Reinforcement Learning on the Web

Deep Q Learning with Keras and Gym

“Deep Reinforcement Learning, Decision Making, and Control

Simple Reinforcement Learning with Tensorflow

Part 0: Q-Learning with Tables and Neural Networks

Part 1 - Two-armed Bandit

Part 2 - Policy-based Agents

Part 3 - Model-Based RL

Part 4: Deep Q-Networks and Beyond

Part 5: Visualizing an Agent’s Thoughts and Actions

Part 6: Partial Observability and Deep Recurrent Q-Networks

Part 7: Action-Selection Strategies for Exploration

Dissecting Reinforcement Learning

REINFORCE tutorial

Deep Q-Learning Recap

Introduction to Reinforcement Learning


Advanced Topics: RL

UCL Course on RL

Berkeley CS 294: Deep Reinforcement Learning

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

CS229 Lecture notes Part XIII: Reinforcement Learning and Control

Practical_RL: A course in reinforcement learning in the wild

Reinforcement Learning (COMP-762) Winter 2017


Playing Atari with Deep Reinforcement Learning

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

Replicating the Paper “Playing Atari with Deep Reinforcement Learning”

A Tutorial for Reinforcement Learning

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Massively Parallel Methods for Deep Reinforcement Learning

Action-Conditional Video Prediction using Deep Networks in Atari Games

Deep Recurrent Q-Learning for Partially Observable MDPs

Continuous control with deep reinforcement learning

Benchmarking for Bayesian Reinforcement Learning

Deep Reinforcement Learning with Double Q-learning

Giraffe: Using Deep Reinforcement Learning to Play Chess

Human-level control through deep reinforcement learning

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

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

MazeBase: A Sandbox for Learning from Games

Learning Simple Algorithms from Examples

Learning Algorithms from Data

Multiagent Cooperation and Competition with Deep Reinforcement Learning

Active Object Localization with Deep Reinforcement Learning

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

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

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

Angrier Birds: Bayesian reinforcement learning

Prioritized Experience Replay

Dueling Network Architectures for Deep Reinforcement Learning

Asynchronous Methods for Deep Reinforcement Learning

Graying the black box: Understanding DQNs

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

Value Iteration Networks

Insights in Reinforcement Learning

Using Deep Q-Learning to Control Optimization Hyperparameters

Continuous Deep Q-Learning with Model-based Acceleration

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

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Benchmarking Deep Reinforcement Learning for Continuous Control

Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning

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

Deep Successor Reinforcement Learning (MIT)

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

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

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

Bayesian Reinforcement Learning: A Survey

Playing FPS Games with Deep Reinforcement Learning

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

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

Learning to Navigate in Complex Environments

Reinforcement Learning with Unsupervised Auxiliary Tasks

Learning to reinforcement learn

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

Exploration for Multi-task Reinforcement Learning with Deep Generative Models

Neural Combinatorial Optimization with Reinforcement Learning

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

Reinforcement Learning Using Quantum Boltzmann Machines

Deep Reinforcement Learning applied to the game Bubble Shooter

Deep Reinforcement Learning: An Overview

Robust Adversarial Reinforcement Learning

Beating Atari with Natural Language Guided Reinforcement Learning

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

Distral: Robust Multitask Reinforcement Learning


Reinforcement Learning: A Survey

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:

Playing Doom

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

Deep Reinforcement Learning From Raw Pixels in Doom

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

Reinforcement Learning via Recurrent Convolutional Neural Networks

Shallow Updates for Deep Reinforcement Learning



General_Deep_Q_RL: General deep Q learning framework

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

Using Deep Q Networks to Learn Video Game Strategies

qlearning4k: Q-learning for Keras

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

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

Chimp: General purpose framework for deep reinforcement learning

Deep Q Learning for ATARI using Tensorflow

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

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

DeeR: DEEp Reinforcement learning framework

KeRLym: A Deep Reinforcement Learning Toolbox in Keras

Pack of Drones: Layered reinforcement learning for complex behaviors

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

Playing Mario with Deep Reinforcement Learning

Deep Attention Recurrent Q-Network

Deep Reinforcement Learning in TensorFlow

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

deep_q_rl: Theano-based implementation of Deep Q-learning


  • 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:

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

Deep Reinforcement Learning for Keras

RL4J: Reinforcement Learning for the JVM

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

dprl: Deep reinforcement learning package for torch7

Reinforcement Learning for Torch: Introducing torch-twrl

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

Tensorflow-Reinforce: Implementation of Reinforcement Learning Models in Tensorflow

deep RL hacking on minecraft with malmo


markovjs: Reinforcement Learning in JavaScript

Deep Q: Deep reinforcement learning with TensorFlow

Deep Q-Learning Network in pytorch

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

Minimal and Clean Reinforcement Learning Examples

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

Play Flappy Bird

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

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

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

MXNET-Scala Playing Flappy Bird Using Deep Reinforcement Learning

Flappy Bird Bot using Reinforcement Learning in Python

Using Keras and Deep Q-Network to Play FlappyBird


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

Pong Neural Network(LIVE)

Tips and Tricks



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:

AgentNet: Deep Reinforcement Learning library for humans

Atari Multitask & Transfer Learning Benchmark (AMTLB)


A Short Introduction To Some Reinforcement Learning Algorithms

A Painless Q-Learning Tutorial

Reinforcement Learning - Part 1

Reinforcement Learning - Monte Carlo Methods

Q-learning with Neural Networks

Guest Post (Part I): 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.

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

Some Reinforcement Learning Algorithms in Python, C++

learning to do laps with reinforcement learning and neural nets

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

Best reinforcement learning libraries?

Super Simple Reinforcement Learning Tutorial

Reinforcement Learning in Python

The Skynet Salesman

Apprenticeship learning using Inverse Reinforcement Learning

Reinforcement Learning and DQN, learning to play from pixels

Deep Learning in a Nutshell: Reinforcement Learning

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

Learning Reinforcement Learning (with Code, Exercises and Solutions)

Deep Reinforcement Learning: Playing a Racing Game

Experimenting with Reinforcement Learning and Active Inference

Deep reinforcement learning, battleship

Deep Learning Research Review Week 2: Reinforcement Learning

Reinforcement Learning: Artificial Intelligence in Game Playing

Artificial Intelligence’s Next Big Step: Reinforcement Learning

Let’s make a DQN

Let’s make a DQN


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:

Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction (Second edition, Draft)

The Self Learning Quant

Reinforcement Learning: An Introduction


Deep Reinforcement Learning Papers

Awesome Reinforcement Learning

Deep Reinforcement Learning Papers

Deep Reinforcement Learning 深度增强学习资源

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

Deep Reinforcement Learning survey

Studying Reinforcement Learning Guide

Reading and Questions

What are the best books about reinforcement learning?

Published: 09 Oct 2015

Recommendation System


Making a Contextual Recommendation Engine


Image-based recommendations on styles and substitutes

A Complex Network Approach for Collaborative Recommendation

Session-based Recommendations with Recurrent Neural Networks

Item2Vec: Neural Item Embedding for Collaborative Filtering

Wide & Deep Learning for Recommender Systems

Hybrid Recommender System based on Autoencoders

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

Collaborative Filtering with Recurrent Neural Networks

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:

Convolutional Matrix Factorization for Document Context-Aware Recommendation

Deep learning for audio-based music recommendation

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

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

Recurrent Recommender Networks

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

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

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

On Sampling Strategies for Neural Network-based Collaborative Filtering

Deep Learning based Recommender System: A Survey and New Perspectives

Training Deep AutoEncoders for Collaborative Filtering


Deep learning for music recommendation

Deep learning for music recommendation and generation


Recommending music on Spotify with deep learning

Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE

Recommending movies with deep learning

Deep Learning Helps iHeartRadio Personalize Music Recommendations

Applying deep learning to Related Pins

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

Building a Music Recommender with Deep Learning


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:

Deep learning recommend system with TensorFlow

Deep Learning Recommender System

Keras Implementation of Recommender Systems


Deep Learning for Recommender Systems

Using MXNet for Recommendation Modeling at Scale


Recommender Systems with Deep Learning


Published: 09 Oct 2015

Classification / Recognition


Published: 09 Oct 2015