链接: 百度网盘 提取码: hj7m AI Engineering: Building Applications with Foundation Models by Chip Huyen: This book is frequently cited as a foundational text, covering the process of building applications using readily available foundation models and how it differs from traditional ML engineering. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen: Often considered complementary to the above, this book focuses on designing scalable, reliable, and maintainable ML systems, from data handling to deployment and monitoring. LLM Engineer’s Handbook: Master the Art of Engineering Large Language Models from Concept to Production by Paul Iusztin & Maxime Labonne: This book offers practical guidance and “recipes” for moving Large Language Model (LLM) projects from prototype to production. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: Praised for its practical approach, this guide covers core ML and deep learning concepts with real-world examples and popular Python libraries. Build a Large Language Model (From Scratch) by Sebastian Raschka: This book is recommended for gaining a deep, fundamental understanding of how LLMs work internally by building one from the ground up. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Often referred to as the “bible” of modern AI foundations, this provides a comprehensive mathematical and conceptual background for deep learning. Prompt Engineering for LLMs by John Berryman & Albert Ziegler: These titles cover techniques for optimizing prompts and model outputs, a critical skill in modern AI development. The AI Engineering Bible by Thomas D. Caldwell: Positioned as a comprehensive reference for contemporary AI engineering practices. Designing Data-Intensive Applications by Martin Kleppmann: Though not exclusively an AI book, it is highly recommended for building scalable and reliable data systems, which is crucial infrastructure for production AI. 3

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