AI Engineering: Foundations, Implementation, and Practice is a comprehensive textbook that takes you from the mathematical foundations of artificial intelligence through to modern production-ready AI systems. This book covers everything you need to understand and build AI applications in today's rapidly evolving landscape.
Starting with the essential mathematics of linear algebra, probability, and optimization, you'll build a solid foundation before diving into classical machine learning algorithms. The book then progresses through deep learning architectures including convolutional neural networks, recurrent networks, and the transformer architecture that powers today's large language models.
Advanced topics include prompt engineering, fine-tuning LLMs, building AI agents, MLOps practices, and deploying AI systems at scale. Whether you're a student entering the field or a professional expanding your skills, this textbook provides the depth and breadth needed to become a proficient AI engineer.