Items related to Machine Learning Fundamentals: A Concise Introduction
- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1108837042
- ISBN 13 9781108837040
- BindingHardcover
- LanguageEnglish
- Number of pages420
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Machine Learning Fundamentals : A Concise Introduction
Published by
Cambridge University Press, 2022
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Machine Learning Fundamentals : A Concise Introduction
Published by
Cambridge University Press, 2022
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Machine Learning Fundamentals: A Concise Introduction
Published by
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ISBN 10: 1108837042
ISBN 13: 9781108837040
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Machine Learning Fundamentals: A Concise Introduction
Published by
Cambridge University Press, 2022
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Machine Learning Fundamentals: A Concise Introduction
Published by
Cambridge University Press, 2022
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Hardcover
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Condition: Good. Good condition. This is the average used book, that has all pages or leaves present, but may include writing. Book may be ex-library with stamps and stickers. 2.33. Seller Inventory # 353-1108837042-gdd
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Machine Learning Fundamentals: A Concise Introduction
Published by
Cambridge University Press, 2022
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Hardcover
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Machine Learning Fundamentals: A Concise Introduction
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ISBN 10: 1108837042
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Machine Learning Fundamentals: A Concise Introduction
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ISBN 10: 1108837042
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Machine Learning Fundamentals: A Concise Introduction
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ISBN 10: 1108837042
ISBN 13: 9781108837040
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Machine Learning Fundamentals (Hardcover)
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Cambridge University Press, Cambridge, 2021
ISBN 10: 1108837042
ISBN 13: 9781108837040
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Hardcover. Condition: new. Hardcover. 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. This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used traditional methods plus recently popular deep learning methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108837040
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