| Description |
1 online resource (xvii, 472 pages) |
| Series |
Sekai hyōjun MIT kyōkasho |
|
世界標準MIT教科書 |
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Sekai hyōjun MIT kyōkasho
|
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世界標準MIT教科書
|
| Summary |
"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 |
| Notes |
Includes indexes |
|
Online resource; title from PDF title page (EBSCO, viewed April 7, 2022) |
| Subject |
Algebras, Linear.
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Mathematical optimization.
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Mathematical statistics.
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Algebras, Linear
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Mathematical optimization
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Mathematical statistics
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880-05 Senkei daisūgaku.
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880-05/$1 線型代数学.
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880-06 Sūri tōkeigaku.
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880-06/$1 数理統計学.
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880-07 Saitekika.
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880-07/$1 最適化.
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880-08 Kikai gakushū.
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880-08/$1 機械学習.
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| Form |
Electronic book
|
| Author |
Matsuzaki, Kiminori, translator
|
|
松崎公紀, translator
|
| ISBN |
9784764972629 |
|
476497262X |
|
