Deep learning: a visual approach
"A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"--
Gespeichert in:
1. Verfasser: | |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
San Francisco, CA
No Starch Press, Inc.
[2021]
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Schlagworte: | |
Online-Zugang: | DE-739 |
Zusammenfassung: | "A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"-- |
Beschreibung: | 1 Online-Ressource (xxviii, 736 Seiten) Illustrationen, Diagramme |
ISBN: | 9781718500730 |
Internformat
MARC
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100 | 1 | |a Glassner, Andrew S. |d 1960- |e Verfasser |0 (DE-588)13836043X |4 aut | |
245 | 1 | 0 | |a Deep learning |b a visual approach |c Andrew Glassner |
264 | 1 | |a San Francisco, CA |b No Starch Press, Inc. |c [2021] | |
300 | |a 1 Online-Ressource (xxviii, 736 Seiten) |b Illustrationen, Diagramme | ||
336 | |b txt |2 rdacontent | ||
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338 | |b cr |2 rdacarrier | ||
505 | 8 | |a Part I: Foundational ideas -- An overview of machine learning -- Essential statistics -- Measuring performance -- Bayes' rule -- Curves and surfaces -- Information theory -- Part II: Basic machine learning -- Classification -- Training and testing -- Overfitting and underfitting -- Data preparation -- Classifiers -- Ensembles -- Part III: Deep learning basics -- Neural networks -- Backpropagation -- Optimizers -- Part IV: Beyond the basics -- Convolutional neural networks -- Convnets in practice -- Autoencoders -- Recurrent neural networks -- Attention and transformers -- Reinforcement learning -- Generative adversarial networks -- Creative applications | |
520 | 3 | |a "A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"-- | |
650 | 0 | 7 | |a Deep Learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
653 | 0 | |a Machine learning | |
653 | 0 | |a Neural networks (Computer science) | |
653 | 0 | |a Artificial intelligence | |
653 | 0 | |a Neural networks (Computer science) | |
653 | 0 | |a Machine learning | |
653 | 6 | |a Handbooks and manuals | |
689 | 0 | 0 | |a Deep Learning |0 (DE-588)1135597375 |D s |
689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
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Datensatz im Suchindex
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adam_text | |
adam_txt | |
any_adam_object | |
any_adam_object_boolean | |
author | Glassner, Andrew S. 1960- |
author_GND | (DE-588)13836043X |
author_facet | Glassner, Andrew S. 1960- |
author_role | aut |
author_sort | Glassner, Andrew S. 1960- |
author_variant | a s g as asg |
building | Verbundindex |
bvnumber | BV047554018 |
classification_rvk | ST 300 ST 301 ST 302 |
collection | ZDB-30-PQE |
contents | Part I: Foundational ideas -- An overview of machine learning -- Essential statistics -- Measuring performance -- Bayes' rule -- Curves and surfaces -- Information theory -- Part II: Basic machine learning -- Classification -- Training and testing -- Overfitting and underfitting -- Data preparation -- Classifiers -- Ensembles -- Part III: Deep learning basics -- Neural networks -- Backpropagation -- Optimizers -- Part IV: Beyond the basics -- Convolutional neural networks -- Convnets in practice -- Autoencoders -- Recurrent neural networks -- Attention and transformers -- Reinforcement learning -- Generative adversarial networks -- Creative applications |
ctrlnum | (OCoLC)1284787468 (DE-599)BVBBV047554018 |
discipline | Informatik |
discipline_str_mv | Informatik |
format | Electronic eBook |
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id | DE-604.BV047554018 |
illustrated | Illustrated |
index_date | 2024-07-03T18:25:09Z |
indexdate | 2025-02-13T09:00:44Z |
institution | BVB |
isbn | 9781718500730 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032929615 |
oclc_num | 1284787468 |
open_access_boolean | |
owner | DE-739 |
owner_facet | DE-739 |
physical | 1 Online-Ressource (xxviii, 736 Seiten) Illustrationen, Diagramme |
psigel | ZDB-30-PQE ZDB-30-PQE UPA_Einzelkauf_NoStarchPress |
publishDate | 2021 |
publishDateSearch | 2021 |
publishDateSort | 2021 |
publisher | No Starch Press, Inc. |
record_format | marc |
spelling | Glassner, Andrew S. 1960- Verfasser (DE-588)13836043X aut Deep learning a visual approach Andrew Glassner San Francisco, CA No Starch Press, Inc. [2021] 1 Online-Ressource (xxviii, 736 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Part I: Foundational ideas -- An overview of machine learning -- Essential statistics -- Measuring performance -- Bayes' rule -- Curves and surfaces -- Information theory -- Part II: Basic machine learning -- Classification -- Training and testing -- Overfitting and underfitting -- Data preparation -- Classifiers -- Ensembles -- Part III: Deep learning basics -- Neural networks -- Backpropagation -- Optimizers -- Part IV: Beyond the basics -- Convolutional neural networks -- Convnets in practice -- Autoencoders -- Recurrent neural networks -- Attention and transformers -- Reinforcement learning -- Generative adversarial networks -- Creative applications "A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"-- Deep Learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning Neural networks (Computer science) Artificial intelligence Handbooks and manuals Deep Learning (DE-588)1135597375 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-7185-0072-3 |
spellingShingle | Glassner, Andrew S. 1960- Deep learning a visual approach Part I: Foundational ideas -- An overview of machine learning -- Essential statistics -- Measuring performance -- Bayes' rule -- Curves and surfaces -- Information theory -- Part II: Basic machine learning -- Classification -- Training and testing -- Overfitting and underfitting -- Data preparation -- Classifiers -- Ensembles -- Part III: Deep learning basics -- Neural networks -- Backpropagation -- Optimizers -- Part IV: Beyond the basics -- Convolutional neural networks -- Convnets in practice -- Autoencoders -- Recurrent neural networks -- Attention and transformers -- Reinforcement learning -- Generative adversarial networks -- Creative applications Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
subject_GND | (DE-588)1135597375 (DE-588)4193754-5 |
title | Deep learning a visual approach |
title_auth | Deep learning a visual approach |
title_exact_search | Deep learning a visual approach |
title_exact_search_txtP | Deep learning a visual approach |
title_full | Deep learning a visual approach Andrew Glassner |
title_fullStr | Deep learning a visual approach Andrew Glassner |
title_full_unstemmed | Deep learning a visual approach Andrew Glassner |
title_short | Deep learning |
title_sort | deep learning a visual approach |
title_sub | a visual approach |
topic | Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
topic_facet | Deep Learning Maschinelles Lernen |
work_keys_str_mv | AT glassnerandrews deeplearningavisualapproach |