Artificial Intelligence: Foundations, Theory, and Algorithms

Zhao, Wayne Xin; Zhou, Kun; Li, Junyi; Tang, Tianyi; Wen, Ji-Rong

Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.

This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.

Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.

The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.

Details

Published by: Springer

Publication Date: 2026-02-03

Format: Hardcover

ISBN-13: 9789819662586

DOI: 10.1007/978-981-96-6259-3

Dimensions: 254cm x178cm

Pages: 466