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[kaggle] M5 Forecasting - Accuracy (4th place solution)
An offline deep reinforcement learning library
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
A continual model for human activity recognition (called HAR-GAN). It is based on a technique called `generative replay` which two models (classifier and generator) are trainined in the same time t…
Notes and exercise solutions for second edition of Sutton & Barto's book
A toolkit for reproducible reinforcement learning research.
PFRL: a PyTorch-based deep reinforcement learning library
Statistical Rethinking Course Winter 2020/2021
ChainerRL is a deep reinforcement learning library built on top of Chainer.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Hands-on Deep Reinforcement Learning, published by Packt
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
A course in reinforcement learning in the wild
Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".
A curated list of action recognition and related area resources
All my source code and dataset for my thesis - Culturally competent Human Activity Recognition with a Pepper robot
A Python library that helps data scientists to infer causation rather than observing correlation.
A re-engagement system which applies different strategies through (non-)verbal behaviors to regain user's attention
Bullet simulation for SoftBank Robotics robots
Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!