内容简介
Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations.
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields.
You'll be able to:
Comfortably speak the languages of AI, machine learning, data science, and mathematics
Unify machine learning models and natural language models under one mathematical structure
Handle graph and network data with ease
Explore real data, visualize space transformations, reduce dimensions, and process images
Decide on which models to use for different data-driven projects
Explore the various implications and limitations of AI
Hala Nelson is an associate professor of mathematics at James Madison University. She has a Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Prior to James Madison University, she was a postdoctoral assistant professor at the University of Michigan, Ann Arbor. She specializes in mathematical modeling and consults for emergency and...
