03-06-2024, 12:48 AM
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![[Image: Chat-GPT2024.png]](https://i.postimg.cc/Jh1bWK9X/Chat-GPT2024.png)
Published 3/2024
Created by Hadi Aghazadeh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 70 Lectures ( 9h 0m ) | Size: 2.9 GB
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All you need to master and apply multi-armed bandit problems into real-world problems
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What you'll learn:
Master all essential Bandit Algorithms
Learn How to Apply Bandit Problems into Real-world Applications with Focus on Product Recommendation
Learn How to Implement All Essential Aspects of Bandit Algorithms in Python
Build Different Deterministic and Stochastic Environments for Bandit Problems to Simulate Different Scenarios
Learn and Apply Bayesian Inference for Bandit Problems and Beyond as a Byproduct of This Course
Understand Essential Concepts in Contextual Bandit Problems
Apply Contextual Bandit Problems in a Real-World Product Recommendation Dataset and Scenario
Requirements:
No obligational pre-requisites
Description:
Welcome to our course where we'll guide you through Multi-armed Bandit Problems and Contextual Bandit Problems, step by step. No prior experience needed - we'll start from scratch and build up your skills so you can use these algorithms for your own projects.We'll cover the basics like random, greedy, e-greedy, softmax, and more advanced methods like Upper Confidence Bound (UCB). Along the way, we'll explain concepts like Regret concept instead of just focusing on rewards value in Reinforcement Learning and Multi-armed Bandit Problems. Through practical examples in different types of environments, like deterministic, stochastic and non-stationary environment, you'll see how these algorithms perform in action.Ever wondered how Multi-armed Bandit problems relate to Reinforcement Learning? We'll break it down for you, highlighting what's similar and what's different.We'll also dive into Bayesian inference, introducing you to Thompson sampling, both for binary reward and real value reward in simple terms, and use Beta and Gaussian distributions to estimate the probability distributions with clear examples to help you understand the theory and how to put it into practice.Then, we'll explore Contextual Bandit problems, using the LinUCB algorithm as our guide. From basic toy examples to real-world data, you'll see how it works and compare it to simpler methods like e-greedy.Don't worry if you're new to Python - we've got you covered with a section to help you get started. And to make sure you're really getting it, we'll throw in some quizzes to test your understanding along the way.Our explanations are clear, our code is clean, and we've added fun visualizations to help everything make sense. So join us on this journey and become a master of Multi-armed and Contextual Bandit Problems!
Who this course is for:
Web Application Developers
Researchers working on Action optimization
Machine Learning Developers and Data Scientists
Startup Enthusiasts Driven to Develop Customized Recommendation Apps.
Homepage
Buy Premium From My Links To Get Resumable Support and Max Speed
Published 3/2024
Created by Hadi Aghazadeh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 70 Lectures ( 9h 0m ) | Size: 2.9 GB
[/center]
[center]
All you need to master and apply multi-armed bandit problems into real-world problems
[/center]
What you'll learn:
Master all essential Bandit Algorithms
Learn How to Apply Bandit Problems into Real-world Applications with Focus on Product Recommendation
Learn How to Implement All Essential Aspects of Bandit Algorithms in Python
Build Different Deterministic and Stochastic Environments for Bandit Problems to Simulate Different Scenarios
Learn and Apply Bayesian Inference for Bandit Problems and Beyond as a Byproduct of This Course
Understand Essential Concepts in Contextual Bandit Problems
Apply Contextual Bandit Problems in a Real-World Product Recommendation Dataset and Scenario
Requirements:
No obligational pre-requisites
Description:
Welcome to our course where we'll guide you through Multi-armed Bandit Problems and Contextual Bandit Problems, step by step. No prior experience needed - we'll start from scratch and build up your skills so you can use these algorithms for your own projects.We'll cover the basics like random, greedy, e-greedy, softmax, and more advanced methods like Upper Confidence Bound (UCB). Along the way, we'll explain concepts like Regret concept instead of just focusing on rewards value in Reinforcement Learning and Multi-armed Bandit Problems. Through practical examples in different types of environments, like deterministic, stochastic and non-stationary environment, you'll see how these algorithms perform in action.Ever wondered how Multi-armed Bandit problems relate to Reinforcement Learning? We'll break it down for you, highlighting what's similar and what's different.We'll also dive into Bayesian inference, introducing you to Thompson sampling, both for binary reward and real value reward in simple terms, and use Beta and Gaussian distributions to estimate the probability distributions with clear examples to help you understand the theory and how to put it into practice.Then, we'll explore Contextual Bandit problems, using the LinUCB algorithm as our guide. From basic toy examples to real-world data, you'll see how it works and compare it to simpler methods like e-greedy.Don't worry if you're new to Python - we've got you covered with a section to help you get started. And to make sure you're really getting it, we'll throw in some quizzes to test your understanding along the way.Our explanations are clear, our code is clean, and we've added fun visualizations to help everything make sense. So join us on this journey and become a master of Multi-armed and Contextual Bandit Problems!
Who this course is for:
Web Application Developers
Researchers working on Action optimization
Machine Learning Developers and Data Scientists
Startup Enthusiasts Driven to Develop Customized Recommendation Apps.
Homepage
Buy Premium From My Links To Get Resumable Support and Max Speed
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