Essential Math for AI. Next-level Mathematics for Efficient and Successful AI Systems.

Abstract

Essential Math for AI Many sectors and 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 current gap in presentation between the unlimited potential and applications of AI and its relevant mathematical foundations. Rather than discussing dense academic theory, author Hala Nelson surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-ofthe-art models. You’ll explore topics such as regression, neural networks, convolution, optimization, probability, Markov processes, differential equations, and more within an exclusive AI context. Engineers, data scientists, mathematicians, and scientists will gain 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