Description A deep dive into the architecture and implementation of AI models capable of logical deduction and multi-step reasoning. It explains how to move beyond basic pattern matching to systems that can "think" through complex queries. Key Topics: Chain-of-thought prompting, reinforcement learning from human feedback (RLHF), and the integration of symbolic logic with neural networks. About the Technology: Reasoning models represent the next stage of Large Language Models (LLMs), focusing on accuracy and verifiable logic in specialized domains. About the Book: Follows a code-centric approach to building a model that performs structured reasoning tasks without relying on massive, opaque datasets. About the Reader: For machine learning engineers and researchers interested in the mechanics of AI cognition and logic. About the Author: Sebastian Raschka is a machine learning researcher and the author of several widely-used Python machine learning textbooks.