Mathematics For Machine Learning
Download Mathematics For Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mathematics For Machine Learning 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.
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Algorithmic Mathematics in Machine Learning
This unique book explores several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. The authors present machine learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader’s understanding; accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text. Algorithmic Mathematics in Machine Learning is intended for mathematicians, computer scientists, and practitioners who have a basic mathematical background in analysis and linear algebra but little or no knowledge of machine learning and related algorithms. Researchers in the natural sciences and engineers interested in acquiring the mathematics needed to apply the most popular machine learning algorithms will also find this book useful. This book is appropriate for a practical lab or basic lecture course on machine learning within a mathematics curriculum.
Mathematics for Machine Learning
"Mathematics for Machine Learning: A Deep Dive into Algorithms" is a comprehensive guide that bridges the gap between mathematical theory and practical applications in the dynamic world of machine learning. Whether you're a data science enthusiast, a budding machine learning engineer, or a seasoned practitioner, this book equips you with the essential mathematical foundations that power cutting-edge algorithms and data-driven insights. Starting with the fundamentals of linear algebra, multivariable calculus, probability, and statistics, Nibedita expertly guides you through the intricate maze of mathematical concepts. From there, you'll explore the depths of linear regression, classification, support vector machines, neural networks, and more, all while unraveling the underlying mathematical principles that make these algorithms tick. This book isn't just about equations and formulas--it's about unlocking the potential of machine learning through a strong mathematical intuition. Nibedita's clear explanations, illustrative examples, and practical insights ensure that you not only grasp the core concepts but also discover how they translate into real-world solutions. Dive into the intricacies of convolutional and recurrent neural networks, grasp the significance of regularization techniques, and explore the ethical dimensions of AI and machine learning. Whether you're seeking to build a solid foundation for a career in data science or aiming to deepen your understanding of machine learning algorithms, "Mathematics for Machine Learning" empowers you to harness the power of mathematics as a tool for innovation and transformation in the digital age.