Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems

This comprehensive guide bridges the gap between AI's potential and its mathematical foundations, providing a solid foundation for success in the field.

With real-world applications and state-of-the-art models, readers will explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more.

The book's conversational style makes it accessible to a broad audience, including engineers, data scientists, mathematicians, scientists, and those early in their careers.

Readers will 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.

Whether you're looking to build a successful career in AI or simply want to understand the underlying math behind this exciting field, this guide is the perfect resource.