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Overview: This book delves into the intricate relationship between mathematics and Machine Learning, providing readers with a comprehensive understanding of the mathematical concepts that underpin modern AI. From linear algebra and calculus to probability theory and statistics, each chapter explores a different mathematical topic and its application in machine learning. Throughout the book, readers will learn about fundamental concepts such as regression, classification, clustering, and Deep Learning, as well as advanced topics like reinforcement learning, GANs, and quantum machine learning. With a focus on both theoretical foundations and practical applications, “Machine Learning Mathematics” is an indispensable resource for anyone looking to deepen their understanding of the mathematical principles that drive contemporary AI algorithms. This book aims to bridge the gap between mathematics and Machine Learning, showcasing the critical role of mathematics in solving complex data-driven tasks. Each chapter presents key mathematical concepts, accompanied by clear explanations and Python code samples, ensuring that readers can grasp the underlying principles.
Genre: Non-Fiction > Tech & Devices
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