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

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