PyTorch to fastai de hajimeru dīpu rāningu: enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch :
PyTorchとfastaiではじめるディープラーニング : エンジニアのためのAIアプリケーション開発 /
"Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accompli...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Weitere beteiligte Personen: | |
| Format: | Elektronisch E-Book |
| Sprache: | Japanisch |
| Veröffentlicht: |
Tōkyō-to Shinjuku-ku
Orairī Japan
2021
|
| Ausgabe: | Shohan. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9784873119427/?ar |
| Zusammenfassung: | "Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions" -- |
| Umfang: | 1 Online-Ressource (584 Seiten) |
| ISBN: | 9784873119427 4873119421 |
Internformat
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| 246 | 3 | 1 | |a Deep learning for coders with fastai and PyTorch : |
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| 880 | 1 | 0 | |6 245-01/Chin |a PyTorchとfastaiではじめるディープラーニング : |b エンジニアのためのAIアプリケーション開発 / |c Jeremy Howard, Sylvain Gugger著 ; 中田秀基訳 = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger. |
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| discipline | Informatik |
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| id | ZDB-30-ORH-078670918 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:20:28Z |
| institution | BVB |
| isbn | 9784873119427 4873119421 |
| language | Japanese |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (584 Seiten) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Orairī Japan |
| record_format | marc |
| spelling | Howard, Jeremy VerfasserIn aut Deep learning for coders with fastai and PyTorch 880-01 PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : Howard Jeremy, Gugger Sylvain cho ; Nakada Hidemoto yaku = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger Deep learning for coders with fastai and PyTorch : 880-02 Dīpu rāningu 880-03 Shohan. 880-04 Tōkyō-to Shinjuku-ku Orairī Japan 2021 1 Online-Ressource (584 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier "Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions" -- Deep learning (Machine learning) Data mining Natural language processing (Computer science) Python (Computer program language) Apprentissage profond Exploration de données (Informatique) Traitement automatique des langues naturelles Python (Langage de programmation) Gugger, Sylvain VerfasserIn aut Nakada, Hidemoto ÜbersetzerIn trl 245-01/Chin PyTorchとfastaiではじめるディープラーニング : エンジニアのためのAIアプリケーション開発 / Jeremy Howard, Sylvain Gugger著 ; 中田秀基訳 = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger. 246-02/Chin ディープラーニング 250-03/Chin 初版. 264-04/Chin 東京都新宿区 オライリー・ジャパン 2021 |
| spellingShingle | Howard, Jeremy Gugger, Sylvain PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : Deep learning (Machine learning) Data mining Natural language processing (Computer science) Python (Computer program language) Apprentissage profond Exploration de données (Informatique) Traitement automatique des langues naturelles Python (Langage de programmation) |
| title | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : |
| title_alt | Deep learning for coders with fastai and PyTorch Deep learning for coders with fastai and PyTorch : Dīpu rāningu |
| title_auth | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : |
| title_exact_search | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : |
| title_full | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : Howard Jeremy, Gugger Sylvain cho ; Nakada Hidemoto yaku = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger |
| title_fullStr | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : Howard Jeremy, Gugger Sylvain cho ; Nakada Hidemoto yaku = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger |
| title_full_unstemmed | PyTorch to fastai de hajimeru dīpu rāningu enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : Howard Jeremy, Gugger Sylvain cho ; Nakada Hidemoto yaku = Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger |
| title_short | PyTorch to fastai de hajimeru dīpu rāningu |
| title_sort | pytorch to fastai de hajimeru dipu raningu enjinia no tame no ai apurikeshon kaihatsu deep learning for coders with fastai and pytorch |
| title_sub | enjinia no tame no AI apurikēshon kaihatsu / = Deep learning for coders with fastai and PyTorch : |
| topic | Deep learning (Machine learning) Data mining Natural language processing (Computer science) Python (Computer program language) Apprentissage profond Exploration de données (Informatique) Traitement automatique des langues naturelles Python (Langage de programmation) |
| topic_facet | Deep learning (Machine learning) Data mining Natural language processing (Computer science) Python (Computer program language) Apprentissage profond Exploration de données (Informatique) Traitement automatique des langues naturelles Python (Langage de programmation) |
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