Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
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Updated
Dec 11, 2019 - Python
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
All codes, both created and optimized for best results from the SuperDataScience Course
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
Thompson Sampling Tutorial
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Optimizing the best Ads using Reinforcement learning Algorithms such as Thompson Sampling and Upper Confidence Bound.
A Julia Package for providing Multi Armed Bandit Experiments
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
Bandit algorithms
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Study of the paper 'Neural Thompson Sampling' published in October 2020
Offline evaluation of multi-armed bandit algorithms
Bayesian Optimization for Categorical and Continuous Inputs
Thompson Sampling for Bandits using UCB policy
The example of using reinforcement learning algorithms in the business, specifically finding what ads to use in our campaign.
Our project for the "Data Intelligence Applications" exam at Politecnico di Milano. The project was about Social Influence and Pricing techniques applied to networks.
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
Codes and templates for ML algorithms created, modified and optimized in Python and R.
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