Machine learning: theory and practice
"Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2023
|
| Ausgabe: | First edition |
| Schriftenreihe: | A Chapman & Hall Book
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples"-- |
| Beschreibung: | Literaturverzeichnis: Seite 275-277 |
| Beschreibung: | xv, 282 Seiten Diagramme |
| ISBN: | 9780367433529 |
Internformat
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| 520 | 3 | |a "Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples"-- | |
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Datensatz im Suchindex
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| author | Kalita, Jugal Kumar |
| author_GND | (DE-588)1038035732 |
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| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | First edition |
| format | Book |
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| id | DE-604.BV050129979 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-21T07:00:19Z |
| institution | BVB |
| isbn | 9780367433529 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035466674 |
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| owner | DE-706 |
| owner_facet | DE-706 |
| physical | xv, 282 Seiten Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | CRC Press |
| record_format | marc |
| series2 | A Chapman & Hall Book |
| spelling | Kalita, Jugal Kumar Verfasser (DE-588)1038035732 aut Machine learning theory and practice Jugal Kalita First edition Boca Raton ; London ; New York CRC Press 2023 xv, 282 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier A Chapman & Hall Book Literaturverzeichnis: Seite 275-277 "Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning Maschinelles Lernen (DE-588)4193754-5 s DE-604 B:DE-89 V:DE-601 pdf/application https://www.gbv.de/dms/tib-ub-hannover/1821223152.pdf 2023-10-25 Inhaltsverzeichnis |
| spellingShingle | Kalita, Jugal Kumar Machine learning theory and practice Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning theory and practice |
| title_auth | Machine learning theory and practice |
| title_exact_search | Machine learning theory and practice |
| title_full | Machine learning theory and practice Jugal Kalita |
| title_fullStr | Machine learning theory and practice Jugal Kalita |
| title_full_unstemmed | Machine learning theory and practice Jugal Kalita |
| title_short | Machine learning |
| title_sort | machine learning theory and practice |
| title_sub | theory and practice |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| url | https://www.gbv.de/dms/tib-ub-hannover/1821223152.pdf |
| work_keys_str_mv | AT kalitajugalkumar machinelearningtheoryandpractice |