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A Handbook
Buch, Englisch, 552 Seiten, Format (B × H): 155 mm x 235 mm
ISBN: 978-981-9255-68-9
Verlag: Springer
This book aims to equip readers with the essential mathematical foundation for machine learning, deep learning, and reinforcement learning. A strong grasp of mathematics is crucial for understanding the underlying principles of these fields. The text systematically introduces key topics such as calculus, linear algebra, probability theory, optimization methods, information theory, stochastic processes, and graph theory—core mathematical concepts essential for mastering machine learning. The material is presented concisely, covering the necessary mathematics with precision and clarity. Through comprehensive explanations and their applications in machine learning, the practical significance of these mathematical tools is clearly demonstrated. By the end of the book, readers will have built a solid mathematical foundation, preparing them for advanced academic research and product development. A prerequisite for this book is a prior study of undergraduate-level calculus, linear algebra, and probability theory.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1 Introduction to Basic Knowledge.- Chapter 2 Linear Algebra.- Chapter 3 Multivariable Calculus.- Chapter 4 Optimization Methods.- Chapter 5 Probability Theory.- Chapter 6 Information Theory.- Chapter 7 Stochastic Processes.- Chapter 8 Graph Theory.
