Mastering Contextual Bandits
A Practical Guide to Advanced Reinforcement Learning Techniques
Created by UlukbekElevate your data science expertise by mastering the sophisticated world of contextual bandit algorithms. This comprehensive guide takes you beyond basic machine learning into the realm of adaptive decision-making systems that learn and optimize in real-time. You'll discover how to build recommendation engines that continuously improve, develop personalized content systems that adapt to user behavior, and create online learning models that balance exploration with exploitation. From foundational concepts like Thompson Sampling and Upper Confidence Bound to cutting-edge techniques involving neural networks and transformer architectures, you'll gain the practical skills needed to implement these powerful algorithms in production environments. The book provides hands-on approaches to solving real-world challenges including cold start problems, feature engineering for contextual data, and evaluation methodologies specific to bandit systems. You'll learn to navigate the complexities of multi-objective optimization, adversarial environments, and large-scale deployment considerations. Whether you're building recommendation systems for e-commerce, optimizing content delivery platforms, or developing adaptive user interfaces, this guide equips you with the advanced reinforcement learning techniques that leading tech companies use to create intelligent, self-improving systems.
Est. Length
315 pages (16 chapters)
Language
English
Publication date
Sep 11, 2025
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