Machine learning fundamentals: a concise introduction
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 wide...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
[2021]
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| Subjects: | |
| Online Access: | BSB01 FHN01 TUM01 UER01 UPA01 Volltext |
| Summary: | 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 |
| Physical Description: | 1 Online-Ressource (xviii, 380 Seiten) |
| ISBN: | 9781108938051 |
| DOI: | 10.1017/9781108938051 |
Staff View
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| discipline | Informatik |
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| doi_str_mv | 10.1017/9781108938051 |
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| isbn | 9781108938051 |
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| spelling | Jiang, Hui Verfasser (DE-588)1247636526 aut Machine learning fundamentals a concise introduction Hui Jiang Cambridge Cambridge University Press [2021] © 2021 1 Online-Ressource (xviii, 380 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Druck-Ausgabe, Hardcover 978-1-108-83704-0 Erscheint auch als Druck-Ausgabe, Paperback 978-1-108-94002-3 https://doi.org/10.1017/9781108938051 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Jiang, Hui Machine learning fundamentals a concise introduction Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning fundamentals a concise introduction |
| title_auth | Machine learning fundamentals a concise introduction |
| title_exact_search | Machine learning fundamentals a concise introduction |
| title_exact_search_txtP | Machine learning fundamentals a concise introduction |
| title_full | Machine learning fundamentals a concise introduction Hui Jiang |
| title_fullStr | Machine learning fundamentals a concise introduction Hui Jiang |
| title_full_unstemmed | Machine learning fundamentals a concise introduction Hui Jiang |
| title_short | Machine learning fundamentals |
| title_sort | machine learning fundamentals a concise introduction |
| title_sub | a concise introduction |
| topic | Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Maschinelles Lernen |
| url | https://doi.org/10.1017/9781108938051 |
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