Predicting the best Ad from the given Ads.
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Updated
May 26, 2022 - Jupyter Notebook
Predicting the best Ad from the given Ads.
Reinforcement learning
A collection of games accompanied by a generalised Monte Carlo Tree Search Artificial Intelligence in combination with Upper Confidence Bounds.
We compare different policies for the checkers game using reinforcement learning algorithms.
LoRa@FIIT algorithms comparison using jupyter notebooks
Web visualisation of the k-armed bandit problem
This repository contains an implementation of checkers where different agents play against each other using different algorithms including Monte Carlo Tree Search, Alpha-Beta Pruning, and Minimax.
Checking CTR(Click Thorugh Rate) of an ad using Thompson Sampling (Reinforcement Lrearning)
Code for the paper "Truncated LinUCB for Stochastic Linear Bandits"
This repo contains code templates of all the machine learning algorithms that are used, like Regression, Classification, Clustering, etc.
Using SciKit Learn few Deep Learning Rules and Algorithms are implemented
Reinforcement learning used in the game of pong
We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.
Optimizing the best Ads using Reinforcement learning Algorithms such as Thompson Sampling and Upper Confidence Bound.
Offline evaluation of multi-armed bandit algorithms
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
A Bayesian global optimization package for material design | Adaptive Learning | Active Learning
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