Isbn: 9798190329117 - reinforcement learning (ai and ml reference handbooks) (3 risultati)

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798190329117

    Serie: Libro 17 di 18 - AI and ML Reference handbooks

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798190329117

    Serie: Libro 17 di 18 - AI and ML Reference handbooks

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  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798190329117

    Serie: Libro 17 di 18 - AI and ML Reference handbooks

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    • Print on Demand

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    Paperback. Condizione: new. Paperback. Master Reinforcement Learning from fundamentals to production-ready AI systems.Whether you're a student, AI engineer, machine learning practitioner, researcher, or interview candidate, this comprehensive reference takes you from the mathematical foundations of Reinforcement Learning to today's cutting-edge applications, including Deep Reinforcement Learning and Reinforcement Learning from Human Feedback (RLHF) used in modern Large Language Models.Designed for both beginners and experienced professionals, this book combines intuitive explanations with rigorous theory, practical Python implementations, interview preparation, and real-world case studies.Inside this book you'll learn: Reinforcement Learning fundamentals and Markov Decision Processes (MDPs)Bellman Equations, Value Functions, Policy Iteration, and Value IterationMonte Carlo Methods, SARSA, Q-Learning, and Temporal Difference LearningExploration vs. Exploitation strategiesDeep Reinforcement Learning with DQN, PPO, SAC, and Actor-Critic methodsModel-Based Reinforcement Learning and Multi-Agent RLRLHF, DPO, and GRPO for Large Language ModelsOffline Reinforcement Learning and Imitation LearningCurrent Reinforcement Learning libraries and ecosystem (2026)Real-world applications in robotics, finance, recommendation systems, autonomous systems, healthcare, and LLM alignmentEnd-to-end capstone projects with production-oriented workflowsInterview questions, coding exercises, knowledge checks, and practical implementation guidanceEvery chapter includes: Beginner-friendly explanationsMathematical intuition and formulasPython examples using modern librariesIndustry best practicesInterview questions and answersHands-on exercises and projectsIf you want a practical, interview-ready, production-focused Reinforcement Learning reference that bridges academic concepts with real-world implementation, this book belongs on your shelf. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.