Artificial Intelligence

Artificial Intelligence by Yu-Gang Jiang; Xingjun Ma; Zuxuan Wu

A compelling, up-to-date guide to the foundations and frontiers of AI, this book brings clarity to complex concepts and practical insight to real-world problems. Whether you are a student beginning your journey, a researcher seeking fresh perspectives, or an engineer applying models in production, this volume balances rigorous theory with actionable techniques.

Start exploring key topics such as machine learning algorithms, deep learning architectures, model training and evaluation, computer vision, and model interpretability—explained with clear examples and thoughtful intuition. The authors break down mathematical ideas without sacrificing depth and illustrate how contemporary AI systems are designed, optimized, and deployed across industries.

You’ll discover how to translate data into robust models, avoid common pitfalls, and evaluate performance in practical settings. Case studies and applied discussions bridge academia and industry, showing how AI drives innovation in healthcare, finance, autonomous systems, and beyond. Emphasis on reproducible workflows and ethical considerations prepares readers to build responsible, scalable solutions.

Ideal for readers worldwide—from North America and Europe to Asia and emerging tech hubs—this book is a go-to resource for mastering AI fundamentals while staying attuned to evolving research directions. Clear, authoritative, and forward-looking, it equips you with the knowledge to design smarter systems and make confident decisions.

Ready to level up your AI expertise? Add Artificial Intelligence to your collection and start transforming ideas into intelligent solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.