Deze Amerikaanse wiskundige staat bekend om zijn baanbrekende bijdragen aan de eindige-elemententheorie, de variatierekening, wavelet-analyse en lineaire algebra. Zijn toewijding aan wiskundeonderwijs blijkt uit zijn productieve output van zeven leerboeken en een monografie. Zijn colleges over inleidende lineaire algebra en computationele wetenschappen zijn gratis toegankelijk, wat zijn inzet benadrukt om wiskundige kennis breder toegankelijk te maken. Zijn werk toont consequent een diepgaand begrip van fundamentele concepten en een innovatieve aanpak om complexe uitdagingen aan te gaan.
Offering a fresh perspective on linear algebra, this book caters to learners of all levels, guiding them from fundamental concepts to advanced topics like singular value decomposition. Its accessible approach ensures that readers can grasp complex ideas easily, making it a valuable resource for students and enthusiasts alike.
Focusing on the intersection of linear algebra and deep learning, this textbook by Professor Gilbert Strang offers a comprehensive course that integrates essential mathematical concepts with practical applications in neural networks. It covers key topics such as the four fundamental subspaces, singular value decompositions, and optimization techniques, along with foundational elements of probability and statistics. The text is designed to be both accessible and rigorous, making it an invaluable resource for students eager to understand how linear algebra underpins modern data learning techniques.
Gilbert Strang's clear, direct style and detailed, intensive explanations make this textbook ideal as both a course companion and for self-study. Single variable and multivariable calculus are covered in depth. Key examples of the application of calculus to areas such as physics, engineering and economics are included in order to enhance students' understanding. New to the third edition is a chapter on the 'Highlights of calculus', which accompanies the popular video lectures by the author on MIT's OpenCourseWare. These can be accessed from math.mit.edu/~gs.
Covering the entire spectrum of computational science and engineering, this book delves into both modeling techniques and solution methods, including analytical and numerical approaches. It serves as a comprehensive resource for understanding the intricacies of computational methods and their applications in various scientific and engineering fields.
Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions, while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and cryptography • A new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (including practice problems, exams, and development of textbook examples), plus codes in MATLAB®, Julia, and Python.