Essential Math For Ai
Download Essential Math For Ai PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Essential Math For Ai book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Essential Math for AI
Author: Hala Nelson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-01-04
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-of-the-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.
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Essential Math for AI
Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions