Taicheng Huang's starred repositories
DeepFaceLab
DeepFaceLab is the leading software for creating deepfakes.
Awesome-LLM
Awesome-LLM: a curated list of Large Language Model
machine_learning_examples
A collection of machine learning examples and tutorials.
machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
rl-baselines-zoo
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Doing_bayesian_data_analysis
Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke
NEAT-Flappy-Bird
An AI that plays flappy bird! Using the NEAT python module.
Reinforcement_learning_tutorial_with_demo
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
Learning-Scientific_Machine_Learning_Residual_Based_Attention_PINNs_DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
Python-EEG-Handbook-CN
Python脑电数据处理中文手册 - A Chinese handbook for EEG data analysis based on Python
DeepRL_Algorithms
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
tool-games
Physics games involving tool use for studying human physical representations and rapid trial-and-error learning
textureSynth
This package contains MatLab code for analyzing and synthesizing digital image of visual texture.
PPO-algo-with-custom-Unity-environment
Implementation of Proximal Policy Optimization algorithm on a custom Unity environment.
flow_toolbox
Optical flow extraction tool using OpenCV
Fast-Texforms
Code database for Fast Texform generation as proposed in the work of Deza, Chen, Long and Konkle (CCN 2019).
reinforcement-learning-tutorials
basic algorithms of reinforcement learning
xyt0098.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes