ssthurai Goto Github PK

hello-world

Working with Git and GitHub repository

how-to-use-quantum-computers

This is repository is to start with Quantum Computing (using IBM Quantum Experience).

hpc-python

Python in High Performance Computing

hubbard-lda3

Implementation of the local density approximation in the 3D Fermi-Hubbard model.

imgbox

infinite-square-well-schrodinger-equation

Analytical solution to the Time-Dependent Schrodinger equation for a particle in an infinite square well

inltk

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need

instagramclone

intermediatepython

iqmol

IQmol is an open source molecular editor and visualization package

jarvis

Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated framework for computational science using density functional theory, classical force-field/molecular dynamics and machine-learning.

jdftx

JDFTx: software for joint density functional theory

julia-scientific-programming

This repo is for learning Julia programming (Coursera)

keras

Deep Learning for humans

khan-api

Documentation for (and examples of) using the Khan Academy API

kwiz

Highly flexible JavaScript quiz engine

lapack

LAPACK development repository

laravel-fiverrclone

Full functional fiverr clone

latexcv

:necktie: A collection of cv and resume templates written in LaTeX. Leave an issue if your language is not supported!

learning-english

Global list of resources for learning English across the web

learning-to-bounce-a-ball-in-virtual-reality

Reinforcement Learning is an active research area which studies the methodological approaches for enabling machines to perform intelligent and expert behaviours. Frameworks of Reinforcement Learning has been utilized for solving various types of problems including games such as Backgammon, Chess or control problems such as inverted pendulum. Here we implement two RL approaches namely Q-Learning and Eligibility Traces for enabling an agent to control the trajectory of a ball with a paddle. First, the game environment is defined as a discrete space and then the aforementioned algorithms are implemented using the MATLAB software. The results indicate that the agent learns in a reasonably fast time and is able to play 4 the game and keep the ball in the air for a considerable number of hits. The results of this project can be compared with human performance in order to infer physiological parameters affecting human learning.In this project a RL based solution for the VR ball bouncing was proposed and tested in MATLAB environment. The results indicate that the agent can learn how to play the game and hit the ball multiple times in a row. Finally, the resulting optimal actions for the state space were implemented into Unity and performed by a virtual agent. In order to achieve this, many tasks had to be completed. The state and action space had to be refined in order to for the project to be computationally feasible. A continuous physics simulation was made for collisions in both Matlab and Unity. The Unity simulation was hooked up to external tracking for player input recording. Multiple iterations of code refinement were performed in order to better improve the performance of the agent in the environment. For example changing the discrete action space was performed in order to ensure that there is no internal constraint for the agent hindering visitation to all states in the environment. A number of graphical means were used to visualize the results from the Q matrix. For example the number of successive hits were plotted in order to examine to which extent the algorithm is able to make the the ball hitting working. Overall this project gave us the opportunity to increase our knowledge of machine learning specially Q-Learning and helped us gain hands on experience for implementation of learning algorithms. Our current results improve through learning but our solution is not as optimal as we imagined it could be. More iterations need to be done on our multiple design decisions in order to gauge the full potential of our model. Ultimately, we were able to implement the broad groundwork needed for an agent to learn to bounce a ball in virtual reality.

learnxinyminutes-docs

Code documentation written as code! How novel and totally my idea!

learnxinyminutes-site

lectures

Oxford Deep NLP 2017 course

linearregression-in-materialsresearch

This project is a part of #FridayNightSimulations.

lowagner.github.io

This is my website.

machine-learning-applications

This is a list of applications of Machine learning

machine-learning-ex

Machine learning exercises.

machine-learning-with-python

Python code for common Machine Learning Algorithms

machinelearning-dinosaurgame

CNN trained to play Google's dinosaur run game.