國立高雄大學圖資館 |

登入

回首頁

Linear algebra and learning from data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Linear algebra and learning from data /Gilbert Strang.
作者:	Strang, Gilbert,
面頁冊數:	xiii, 432 pages :illustrations ;25 cm
標題:	Mathematical optimization
ISBN:	0692196382

Linear algebra and learning from data /
Strang, Gilbert,

Linear algebra and learning from data /Gilbert Strang. - xiii, 432 pages :illustrations ;25 cm

Includes bibliographical references and indexes.

Deep learning and neural nets -- Preface and acknowledgements -- Part I: Highlights of linear algebra -- Part II: Computations with large matrices -- Part III: Low rank and compressed sensing -- Part IV: Special matrices -- Part V: Probability and statistics -- Part IV: Optimization -- Part VII: Learning from data -- Books on machine learning -- Eigenvalues and singular values : rank one -- Codes and algorithms for numerical linear algebra -- Counting parameters in the basic factorizations -- Index of authors -- Index -- Index of symbols.

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. -- Publisher's description.

ISBN: 0692196382

Standard Tech. Report. No.: cam 2200313Ii 4500Subjects--Topical Terms:

252884
Mathematical optimization
Index Terms--Genre/Form:

708244
Textbooks.

LC Class. No.: QA184.2 / .S77 2019

Dewey Class. No.: 512.5

Linear algebra and learning from data /
LDR:02780nam a2200325 a 4500 001 616690
003 OCoLC
005 20221006041024.0
008 221006s2019 maua b 001 0 eng d
019 $a 1201644281
020 $a 0692196382 $q (hardback)
020 $a 9780692196380 $q (hardback)
027 4 4 $a cam 2200313Ii 4500
029 1 $a AU@ $b 000065403807
035 $a (OCoLC)1081372892 $z (OCoLC)1201644281
035 $a on1081372892
040 $a YDX $b eng $e rda $e pn $c YDX $d SEA $d PUL $d OCLCF $d NLHHG $d PAU $d OCLCO $d STF $d OCLCO $d UPM $d OCLCO $d IPL $d GUA $d OCLCO $d P4A $d COO $d OCLCO $d BCD $d GRU $d ORZ $d NLMVD $d U9X $d OCLCQ $d BWBCA $d OCLCQ $d IL4J6 $d Z#U
049 $a NUKM
050 4 $a QA184.2 $b .S77 2019
082 0 4 $a 512.5 $2 23/eng/20220902
100 1 $a Strang, Gilbert, $e author. $3 916217
245 1 0 $a Linear algebra and learning from data / $c Gilbert Strang.
264 1 $a Wellesley, MA : $b Wellesley-Cambridge Press, $c [2019]
300 $a xiii, 432 pages : $b illustrations ; $c 25 cm
336 $a text $b txt $2 rdacontent
337 $a unmediated $b n $2 rdamedia
338 $a volume $b nc $2 rdacarrier
504 $a Includes bibliographical references and indexes.
505 0 $a Deep learning and neural nets -- Preface and acknowledgements -- Part I: Highlights of linear algebra -- Part II: Computations with large matrices -- Part III: Low rank and compressed sensing -- Part IV: Special matrices -- Part V: Probability and statistics -- Part IV: Optimization -- Part VII: Learning from data -- Books on machine learning -- Eigenvalues and singular values : rank one -- Codes and algorithms for numerical linear algebra -- Counting parameters in the basic factorizations -- Index of authors -- Index -- Index of symbols.
520 $a 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. -- Publisher's description.
650 0 $a Mathematical optimization $3 252884
650 0 $a Algebras, Linear $v Popular works. $3 397301
650 0 $a Mathematical statistics $3 182294
650 7 $a Algebras, Linear. $3 183087
650 7 $a Mathematical optimization. $3 183292
650 7 $a Mathematical statistics. $3 181877
650 7 $a Lineaire algebra. $2 gtt $3 916252
650 7 $a Wiskundige methoden. $2 gtt $3 916253
655 7 $a Textbooks. $2 fast $3 708244
655 7 $a Leermiddelen (vorm) $2 gtt $3 788711
938 $a YBP Library Services $b YANK $n 15962728
994 $a C0 $b TWNUK