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rec_rnn

Adaptive Attention Network based on LSTM for Recommender Systems. Implemented with TensorFlow

recurrent-transformer

[ACL 2020] PyTorch code for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

recurrent-visual-attention

A PyTorch Implementation of "Recurrent Models of Visual Attention"

recurrent_gsn

Deep Generative Stochastic Networks for Sequence Prediction

recurrent_neural_net_demo

Recurrent Neural Net Demo code for Build an RNN in 5 Min on Youtube

recurrent_recommender_networks

An implementation of "Recurrent Recommender Networks" with Tensorflow.

recurrenthighwaynetworks

Recurrent Highway Networks - Implementations for Tensorflow, Torch7, Theano and Brainstorm

recurrentshop

Framework for building complex recurrent neural networks with Keras

recursiveneuraltensornetwork

A deep learning recursive neural tensor network for sentiment analysis

redash

Make Your Company Data Driven. Connect to any data source, easily visualize and share your data.

reducedvariancereparamgradients

Reducing Reparameterization Gradient Variance code.

reduction

Reduction is a python script which automatically summarizes a text by extracting the sentences which are deemed to be most important.

reference

Reference implementations of MLPerf benchmarks

referringrelationships

refinedet

Single-Shot Refinement Neural Network for Object Detection

reformulation-aware-metrics

Our CIKM21 Paper "Incorporating Query Reformulating Behavior into Web Search Evaluation"

refresh

Ranking Sentences for Extractive Summarization with Reinforcement Learning

refuse-collection-vehicle-route-optimization

Vehicle route optimization used for a refuse collection vehicle simulation

region-ensemble-network

Repository for Region Ensemble Network based Hand Pose Estimation

regionalcorrelation

regression_forest

Random regression forests for audio event detection

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

reinforcement-learning-1

Reinforcement Learning Project

reinforcement-learning-2

Minimal and Clean Reinforcement Learning Examples

reinforcement-learning-3

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.

reinforcement-learning-agents

Play various of games using deep learning Tensorflow and deep Evolution Strategies

reinforcement-learning-algorithms-and-dynamic-programming

Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value Function Approximation were applied to stabilize an inverted pendulum system and achieve optimal control. So essentially, the concept of Reinforcement Learning Controllers has been established. The Reinforcement Learning Controllers have been compared on the basis of performance and efficiency and they are separately compared with the classical Linear Quadratic Regulator Controller. Each of the RL controller have been integrated with a Swing up controller. A virtual switch toggles between the Swing up controller and the RL controller automatically, based on the value of the angular deviation theta with respect to the vertical plane. My research paper and my undergraduate thesis have been uploaded for reference. All the codes have also been uploaded.

reinforcement-learning-an-introduction

Python implementation of Reinforcement Learning: An Introduction

reinforcement-learning-based-encoder-decoder-implementation-

Implementation of the paper "A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules"

reinforcement-learning-cvrp

Dynamic Attention Encoder-Decoder model to learn and design heuristics to solve capacitated vehicle routing problems