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A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models Sandip K

(ebook) (audiobook) (audiobook) Język publikacji: angielski
A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models Sandip K - okladka książki

A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models Sandip K - okladka książki

A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models Sandip K - audiobook MP3

A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models Sandip K - audiobook CD

Autor:
Sandip K
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach to aligning AI systems with human values. By combining reinforcement learning with human input, RLHF has become a critical methodology for improving the safety and reliability of large language models (LLMs).

This book begins with the foundations of reinforcement learning, including key algorithms such as proximal policy optimization, and shows how reward models integrate human preferences to fine-tune AI behavior. You’ll gain a practical understanding of how RLHF optimizes model parameters to better match real-world needs.

Beyond theory, you’ll explore strategies for collecting preference data, training reward models, and enhancing LLM fine-tuning workflows. Common challenges such as cost, bias, and scalability are addressed with practical solutions and AI-driven alternatives.

The final chapters cover emerging methods, advanced evaluation, and AI safety. By the end, you’ll be equipped with the knowledge and skills to apply RLHF across domains, building AI systems that are powerful, trustworthy, and aligned with human values.

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O autorze książki

Sandeep (Sandip) Kulkarni is a Principal Applied AI Engineer at Microsoft, where he builds LLM- and RL-powered solutions across Azure Data and Microsoft Fabric. His work spans real-time control, simulators, and LLMOps, with deployments from heavy equipment to chemical processing. Previously at Bonsai and Western Digital, he led simulation and control initiatives. He holds a PhD in Control Engineering (University of Utah) and an MS in Dynamical Systems & Control (UC Davis).

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