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...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
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| Links: | https://doi.org/10.1017/9781108938051 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xviii, 380 Seiten) |
| ISBN: | 9781108938051 |
Internformat
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| id | ZDB-20-CTM-CR9781108938051 |
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| indexdate | 2025-03-03T11:58:08Z |
| institution | BVB |
| isbn | 9781108938051 |
| language | English |
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| spelling | Jiang, Hui Machine learning fundamentals a concise introduction Hui Jiang, York University, Toronto Cambridge Cambridge University Press 2021 1 Online-Ressource (xviii, 380 Seiten) txt c cr 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. Erscheint auch als Druck-Ausgabe 9781108837040 Erscheint auch als Druck-Ausgabe 9781108940023 |
| spellingShingle | Jiang, Hui Machine learning fundamentals a concise introduction |
| 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_full | Machine learning fundamentals a concise introduction Hui Jiang, York University, Toronto |
| title_fullStr | Machine learning fundamentals a concise introduction Hui Jiang, York University, Toronto |
| title_full_unstemmed | Machine learning fundamentals a concise introduction Hui Jiang, York University, Toronto |
| title_short | Machine learning fundamentals |
| title_sort | machine learning fundamentals a concise introduction |
| title_sub | a concise introduction |
| work_keys_str_mv | AT jianghui machinelearningfundamentalsaconciseintroduction |