Essential Math for AI: Next-Level Mathematics for Developing Efficient and Successful AI Systems
Description
Hala Nelson
ISBNs: 1098107632, 978-1098107611, 9781098107611, 978-1098107635, 9781098107635, B0BRNYDGQR
English | 2023 | Original PDF | 30 MB | 605 Pages
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
