Master the mathematical foundations of the AI revolution with Linear Algebra and Learning from Data. Written by world-renowned MIT professor Gilbert Strang, this seminal text provides the essential link between classical linear algebra and the modern world of deep learning and data science. At Nybookshub, we are committed to providing the elite academic literature and technical resources that empower students, engineers, and researchers to understand the "why" behind today's most powerful algorithms. About the Book Linear Algebra and Learning from Data is the first textbook to teach the mathematics of deep learning from the ground up. Professor Strang transitions seamlessly from the basics of linear algebra—vectors, matrices, and subspaces—to the complex structures of neural networks. This first edition covers indispensable topics such as Singular Value Decomposition (SVD), backpropagation, and stochastic gradient descent. It is an essential volume for anyone looking to go beyond "black box" machine learning and develop a rigorous mathematical intuition for how data is processed and learned. What You’ll Learn / Why Read This manual equips you with a deep understanding of the five fundamental factorizations of a matrix and their applications in data compression and pattern recognition. You will learn the mechanics of convolutional neural networks (CNNs), the importance of low-rank approximations, and the geometry of high-dimensional data. The book highlights the optimization techniques that allow machines to learn from massive datasets. By engaging with Strang’s famous "Four Fundamental Subspaces" framework, you will gain the clarity needed to design and troubleshoot advanced machine learning architectures. Author Bio Gilbert Strang is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT). He is a fellow of the American Academy of Arts and Sciences and a recipient of the Chauvenet Prize and the Steele Prize for Exposition. His MIT OpenCourseWare lectures on Linear Algebra have been viewed by millions worldwide, making him one of the most respected and beloved math educators in history. Product Details Author: Gilbert Strang Publisher: Wellesley-Cambridge Press Language: English Format: Hardcover / Professional Edition ISBN-13: 978-0692196380 Genre: Mathematics / Data Science / Machine Learning Pages: 430+ pages Why Buy from Nybookshub Nybookshub is your premier source for authentic, high-level technical and academic literature. We understand that for serious students of technology and mathematics, having the authoritative work of masters like Gilbert Strang is vital. Benefit from our professional service and reliable global shipping to the US, UK, Australia, and beyond. We take pride in delivering the knowledge that powers the future of technology, ensuring every order is handled with the professional care your studies deserve. Frequently Asked Questions Is this book suitable for beginners in Linear Algebra? While it covers the basics, it moves quickly into advanced topics. It is best suited for those who have had an introductory course or are highly motivated to learn the math specifically for data science. Does it cover Deep Learning? Yes, the latter half of the book is dedicated to the mathematics of neural networks, including the architecture of layers and the calculus of backpropagation. Is this the official Wellesley-Cambridge Press edition? Yes, this is the authentic first edition published by Professor Strang’s own press, ensuring the highest quality of content and layout. Does it include exercises? Yes, the book includes thought-provoking problems and exercises at the end of each section to test your understanding of the concepts. Does Nybookshub ship technical math books to the UK and Australia? We offer secure and tracked international shipping to ensure that researchers and students worldwide can access Professor Strang’s latest work. Linear Algebra and Learning from Data, Gilbert Strang, 978-0692196380, MIT Mathematics, Machine Learning Theory, Neural Networks, SVD, Data Science Textbook, Deep Learning Math, Singular Value Decomposition, Nybookshub, Technical Literature, Global Shipping Books, Academic Resources.People also read/purchase: Mathematics for Machine Learning – Deisenroth & Faisal Paperback |9781108455145|Deep Learning: Foundations and Concepts – Christopher Bishop |9783031454677|
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