Machine Learning Fundamentals by Jiang (45 results)

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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    Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

    • Softcover

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    Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press CUP, 2022

    1108940021 / 9781108940023

    • Softcover

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    Condition: New. pp. 418.

  • Language: English

    Published by Cambridge University Press, 2021

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, GB, 2021

    1108940021 / 9781108940023

    • Softcover

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    Paperback. Condition: New. 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.

  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, 2021

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

    • Softcover

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  • Language: English

    Published by Cambridge University Press, 2021

    1108940021 / 9781108940023

    • Softcover

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    Paperback. Condition: Brand New. 400 pages. 10.00x8.00x1.00 inches. In Stock.

  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2021

    1108940021 / 9781108940023

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  • Language: English

    Published by Cambridge University Press, 2022

    1108837042 / 9781108837040

    • Hardcover

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  • Language: English

    Published by Cambridge University Press, 2022

    1108837042 / 9781108837040

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  • Language: English

    Published by Cambridge University Pr., 2022

    1108940021 / 9781108940023

    • Softcover

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    Condition: New. 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 t.

  • Language: English

    Published by Cambridge University Press, 2022

    1108837042 / 9781108837040

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  • Language: English

    Published by Cambridge University Press, 2022

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  • Language: English

    Published by Cambridge University Press, 2022

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  • Language: English

    Published by Cambridge University Press, 2021

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  • Language: English

    Published by Cambridge University Press, 2022

    1108837042 / 9781108837040

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  • Language: English

    Published by Cambridge University Press, GB, 2021

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    Paperback. Condition: New. 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.

  • Language: English

    Published by Cambridge University Press, 2022

    1108837042 / 9781108837040

    • Hardcover

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  • Language: English

    Published by Cambridge University Press, 2022

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  • Language: English

    Published by Cambridge University Press, 2022

    1108940021 / 9781108940023

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    Taschenbuch. Condition: 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.