A fast Evolution Strategy implementation in Python
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Updated
Apr 27, 2020 - Python
A fast Evolution Strategy implementation in Python
workbench for learing&practising AI tech in real scenario on Android device, powered by GGML(Georgi Gerganov Machine Learning) and NCNN(Tencent NCNN) and FFmpeg
An AI agent Learning to play Flappy Bird using Evolution Strategies and deep learning models.
A starter bot written in python for the pokerwars.io platform. To play: pull this code, register on pokerwars.io, get your API token and play!
A starter bot written in Java for the pokerwars.io platform. To play: pull this code, register on pokerwars.io, get your API token and play!
AI学习路线,入门-基础-进阶-高级
A starter bot written in javascript for the pokerwars.io platform. To play: pull this code, register on pokerwars.io, get your API token and play!
This is a starting point for my ( and can be yours ) Machine Learning journey, This Repo consist all the Prerequisites and Resource Links you need to master ML
A tool to extract plain (unformatted) multilingual text, redirects, links and categories from wikipedia backups (dumps). Designed to prepare clean training data for AI training / Machine Learning software.
This repository provides the latest quizzes and programming assignments from the Deep Learning Specialization. I spent over two months, from September to November, completing this course, and it was both incredibly rewarding and challenging. If you find yourself stuck at any point in your own journey, feel free to refer to my results :3
coding projects for controling the AI learning robot; Misty I. || https://mistyrobotics.com
🎹 Pianotes - Sharing the Joy of Music Education & Community.
Various assignments relating to the practice of AI concepts, specifically: search, logic, learning, and uncertainty.
A playground to test brain.js classification based on various datasets
missionaries and cannibals problem as desribed to learn to use the aima library
AI-based Evolutions Simulation in the browser
This project features a PC vs. PC Tic-Tac-Toe game based on evaluation function which learns from self-play. It uses six features to represent the board state, which influence the linear evaluation function over time.
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