Sutorangu: senkei daisū to dēta saiensu /:
ストラング:線形代数とデータサイエンス /
"This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most importan...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | Japanese English |
| Veröffentlicht: |
Tōkyō-to Shinjuku-ku :
Kindai Kagakusha,
2021.
|
| Ausgabe: | Shohan. |
| Schriftenreihe: | Sekai hyōjun MIT kyōkasho.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text." -- |
| Beschreibung: | Includes indexes. |
| Beschreibung: | 1 online resource (xvii, 472 pages) |
| ISBN: | 9784764972629 476497262X |
Internformat
MARC
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| 264 | 1 | |6 880-03 |a Tōkyō-to Shinjuku-ku : |b Kindai Kagakusha, |c 2021. | |
| 300 | |a 1 online resource (xvii, 472 pages) | ||
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| 650 | 0 | |a Algebras, Linear. |0 http://id.loc.gov/authorities/subjects/sh85003441 | |
| 650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
| 650 | 0 | |a Mathematical statistics. |0 http://id.loc.gov/authorities/subjects/sh85082133 | |
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| 880 | |6 250-02/$1 |a 初版. | ||
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| 880 | |6 520-00/$1 |a "データサイエンティストが知っているべき，情報時代に必須の線形代数教科書！ 本書は，『ストラング：線形代数イントロダクション』の原著者ギルバート・ストラングMIT教授が，データサイエンスの基礎を成す数学（線形代数，確率・統計，最適化）を解説した専門書． データサイエンスの要となるのはニューラルネットワークおよび深層学習であり，その根幹を理解するために線形代数を深く学ぶことが重要となる． 深層学習の解説書は多数あるが，その根底にある数学まで徹底的に解説した書籍はほとんどない． 本書は，線形代数の発展的教科書として，またデータサイエンティストを志す読者が線形代数を学ぶための教科書としてふさわしい一冊である．"-- |c Provided by publisher. | ||
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| 880 | |6 758-00/$1 |i has work: |a ストラング:線形代数とデータサイエンス (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXT9PmGRBXKRW94JHjcWrC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| _version_ | 1816882558858690560 |
| adam_text | |
| any_adam_object | |
| author | Strang, Gilbert |
| author2 | Matsuzaki, Kiminori |
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| author_GND | http://id.loc.gov/authorities/names/n80061807 |
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| discipline | Mathematik |
| edition | Shohan. |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1306380202 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:32Z |
| institution | BVB |
| isbn | 9784764972629 476497262X |
| language | Japanese English |
| oclc_num | 1306380202 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
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| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Kindai Kagakusha, |
| record_format | marc |
| series | Sekai hyōjun MIT kyōkasho. |
| series2 | Sekai hyōjun MIT kyōkasho |
| spelling | Strang, Gilbert, author. http://id.loc.gov/authorities/names/n80061807 Linear algebra and learning from data. Japanese 880-01 Sutorangu: senkei daisū to dēta saiensu / Girubāto Sutorangu cho ; Matsuzaki Kiminori yaku = Linear algebra and learning from data / Gilbert Strang. 880-02 Shohan. 880-03 Tōkyō-to Shinjuku-ku : Kindai Kagakusha, 2021. 1 online resource (xvii, 472 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 880-04 Sekai hyōjun MIT kyōkasho Online resource; title from PDF title page (EBSCO, viewed April 7, 2022). Includes indexes. "This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text." -- Provided by publisher. Algebras, Linear. http://id.loc.gov/authorities/subjects/sh85003441 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Mathematical statistics. http://id.loc.gov/authorities/subjects/sh85082133 Algèbre linéaire. Optimisation mathématique. Algebras, Linear fast Mathematical optimization fast Mathematical statistics fast 880-05 Senkei daisūgaku. jlabsh/4 880-06 Sūri tōkeigaku. jlabsh/4 880-07 Saitekika. jlabsh/4 880-08 Kikai gakushū. jlabsh/4 880-09 Matsuzaki, Kiminori, translator. Translation of: Strang, Gilbert. Linear algebra and learning from data. Wellesley, MA : Wellesley-Cambridge Press, [2019] 0692196382 (OCoLC)1081372892 880-10 Sekai hyōjun MIT kyōkasho. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3077962 Volltext 245-01/$1 ストラング:線形代数とデータサイエンス / ギルバート・ストラング著 ; 松崎公紀訳 = Linear algebra and learning from data / Gilbert Strang. 250-02/$1 初版. 264-03/$1 東京都新宿区 : 近代科学社, 2021. 490-04/$1 世界標準MIT教科書 520-00/$1 "データサイエンティストが知っているべき，情報時代に必須の線形代数教科書！ 本書は，『ストラング：線形代数イントロダクション』の原著者ギルバート・ストラングMIT教授が，データサイエンスの基礎を成す数学（線形代数，確率・統計，最適化）を解説した専門書． データサイエンスの要となるのはニューラルネットワークおよび深層学習であり，その根幹を理解するために線形代数を深く学ぶことが重要となる． 深層学習の解説書は多数あるが，その根底にある数学まで徹底的に解説した書籍はほとんどない． 本書は，線形代数の発展的教科書として，またデータサイエンティストを志す読者が線形代数を学ぶための教科書としてふさわしい一冊である．"-- Provided by publisher. 650-05/$1 線型代数学. jlabsh/4 650-06/$1 数理統計学. jlabsh/4 650-07/$1 最適化. jlabsh/4 650-08/$1 機械学習. jlabsh/4 700-09/$1 松崎公紀, translator. 758-00/$1 has work: ストラング:線形代数とデータサイエンス (Text) https://id.oclc.org/worldcat/entity/E39PCXT9PmGRBXKRW94JHjcWrC https://id.oclc.org/worldcat/ontology/hasWork 830-10/$1 世界標準MIT教科書. |
| spellingShingle | Strang, Gilbert Sutorangu: senkei daisū to dēta saiensu / Sekai hyōjun MIT kyōkasho. Algebras, Linear. http://id.loc.gov/authorities/subjects/sh85003441 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Mathematical statistics. http://id.loc.gov/authorities/subjects/sh85082133 Algèbre linéaire. Optimisation mathématique. Algebras, Linear fast Mathematical optimization fast Mathematical statistics fast 880-05 Senkei daisūgaku. jlabsh/4 880-06 Sūri tōkeigaku. jlabsh/4 880-07 Saitekika. jlabsh/4 880-08 Kikai gakushū. jlabsh/4 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85003441 http://id.loc.gov/authorities/subjects/sh85082127 http://id.loc.gov/authorities/subjects/sh85082133 |
| title | Sutorangu: senkei daisū to dēta saiensu / |
| title_alt | Linear algebra and learning from data. |
| title_auth | Sutorangu: senkei daisū to dēta saiensu / |
| title_exact_search | Sutorangu: senkei daisū to dēta saiensu / |
| title_full | Sutorangu: senkei daisū to dēta saiensu / Girubāto Sutorangu cho ; Matsuzaki Kiminori yaku = Linear algebra and learning from data / Gilbert Strang. |
| title_fullStr | Sutorangu: senkei daisū to dēta saiensu / Girubāto Sutorangu cho ; Matsuzaki Kiminori yaku = Linear algebra and learning from data / Gilbert Strang. |
| title_full_unstemmed | Sutorangu: senkei daisū to dēta saiensu / Girubāto Sutorangu cho ; Matsuzaki Kiminori yaku = Linear algebra and learning from data / Gilbert Strang. |
| title_short | Sutorangu: senkei daisū to dēta saiensu / |
| title_sort | sutorangu senkei daisu to deta saiensu |
| topic | Algebras, Linear. http://id.loc.gov/authorities/subjects/sh85003441 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Mathematical statistics. http://id.loc.gov/authorities/subjects/sh85082133 Algèbre linéaire. Optimisation mathématique. Algebras, Linear fast Mathematical optimization fast Mathematical statistics fast 880-05 Senkei daisūgaku. jlabsh/4 880-06 Sūri tōkeigaku. jlabsh/4 880-07 Saitekika. jlabsh/4 880-08 Kikai gakushū. jlabsh/4 |
| topic_facet | Algebras, Linear. Mathematical optimization. Mathematical statistics. Algèbre linéaire. Optimisation mathématique. Algebras, Linear Mathematical optimization Mathematical statistics Senkei daisūgaku. Sūri tōkeigaku. Saitekika. Kikai gakushū. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3077962 |
| work_keys_str_mv | AT stranggilbert linearalgebraandlearningfromdata AT matsuzakikiminori linearalgebraandlearningfromdata AT stranggilbert sutorangusenkeidaisutodetasaiensu AT matsuzakikiminori sutorangusenkeidaisutodetasaiensu |