A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
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Hui Jiang was born and raised in China, and she came to the United States as a graduate student in the early 1990s. Over the years, she worked as a professional in various industries and now owns a successful business. The author has lived in Texas for more than thirty years, where she raised two wonderful children.
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Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. Artikel-Nr. V9781108837040
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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 400 pages. 10.20x8.15x1.06 inches. In Stock. Artikel-Nr. x-1108837042
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely 'from scratch' based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts. Artikel-Nr. 9781108837040
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