chaoshunh

2d-tomography

This repository is a 2D travel-time tomography seismic using MATLAB which I build and my friend Rinta in order to complete my Final Projects. The forward modelling is resolved using Fast Marching Method (FMM) with finite difference approximation and the raytracing is resolved based on John Vidale paper, Finite difference calculation of travel times. This code can use two method in inversion part, Least Square and Pseudo-Inverse where the input data is only needed travel time and the location of station (in UTM, both easting and northing). You can also set some parameters which could affects the tomography result, such as the number of iteration, displaying forward modelling or inverse modelling to track your data, save your model or not and etc.

ContinuousMaze

Computational Neuroscience Course: RL in continuous state and action space

d2geo

Framework for computing seismic attributes with Python.

darknet

Convolutional Neural Networks

DDQN-PER

Double Deep Q-Learning with Prioritized Experience Replay

deep-reinforcement-learning

Programming Assignments and Lectures for UC Berkeley's CS 294: Deep Reinforcement Learning

Domain-Adaptation-Using-Optimal-Transport-Filter-Techniques-as-Wavelet-Transform-in-Speech-Recogniti

Domain adaptation using Optimal Transport as Loss Function using Generative Adversarial Networks on regression and classification problems.

FD-RTM-FWI-2018.01.31backup

Finite difference forward modeling, Full waveform inversion, reverse time migration, raytracing, CUDA, MPICH, C, OpenMP, GTK, GUI, Hybrid Programming, backup

MSDS6371401

PTGPBO

Bayesian Optimisation with Gaussian Processes implemented in PyTorch

chaoshunh/PTGPBO

Bayesian Optimisation with Gaussian Processes implemented in PyTorch

chaoshunh/2d-tomography

This repository is a 2D travel-time tomography seismic using MATLAB which I build and my friend Rinta in order to complete my Final Projects. The forward modelling is resolved using Fast Marching Method (FMM) with finite difference approximation and the raytracing is resolved based on John Vidale paper, Finite difference calculation of travel times. This code can use two method in inversion part, Least Square and Pseudo-Inverse where the input data is only needed travel time and the location of station (in UTM, both easting and northing). You can also set some parameters which could affects the tomography result, such as the number of iteration, displaying forward modelling or inverse modelling to track your data, save your model or not and etc.

chaoshunh/acr-build-helloworld-node

Sample Node.js application for use in demonstrating Azure Container Registry Build.

chaoshunh/Apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

chaoshunh/Carla_Ray_Rlib

Carla-Simulator environment compatible with Ray/Rllib

chaoshunh/CS420_Machine_Learning

course repo

chaoshunh/DeepLearning.ai-Summary

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

chaoshunh/edc2019-docker

EDC 2019 Docker workshop

chaoshunh/gcn

Implementation of Graph Convolutional Networks in TensorFlow

chaoshunh/homework

Assignments for CS294-112.

chaoshunh/info8010-deep-learning

Lectures for INFO8010 - Deep Learning, ULiège

chaoshunh/ipyparallel

Interactive Parallel Computing in Python

chaoshunh/Leetcode

Play Leetcode with different Programming language

chaoshunh/Lina-Seismic-Playground

Machine Learning/ Deep Learning processing Seismic data

chaoshunh/MLOps

MLOps examples

chaoshunh/MtL-Progress-github.io

Repository to track the progress in Meta-Learning (MtL), including the datasets and the current state-of-the-art for the most common MtL problems.

chaoshunh/NTU-ReinforcementLearning-Notes

国立**大学李宏毅老师讲解的深度强化学习学习笔记

chaoshunh/panda3d-tutorial

A Tutorial about game creation with the Panda3D game engine

chaoshunh/pyod

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

chaoshunh/sagemaker_Ray_RLlib_custom_env

Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).

chaoshunh/seglearn

Python module for machine learning time series:

chaoshunh/SeismicPro

Seismic data processing with ML and deep learning

chaoshunh/ServerParallelAgent

Server Agent Using Ray

chaoshunh/slides

chaoshunh/SLIMpy-contrib

SLIMpy public contrib

chaoshunh/system_design

Preparation links and resources for system design questions

chaoshunh/T3D-keras

A Temporal 3D DNN for action recognition in videos

chaoshunh/TrainRBFNetworkWithES

chaoshunh/USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-Learning

chaoshunh/ZSL_Relation

Code Repo for 10-708 project