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Geometry of deep learning : a signal processing perspective

Geometry of deep learning : a signal processing perspective (Loan 1 times)

Material type
단행본
Personal Author
예종철.
Title Statement
Geometry of deep learning : a signal processing perspective / Jong Chul Ye.
Publication, Distribution, etc
Singapore :   Springer,   2022.  
Physical Medium
xvi, 330 p. : col. ill. ; 24 cm.
Series Statement
Mathematics in industry ;37.
ISBN
9789811660450
Bibliography, Etc. Note
Includes bibliographical references (p. 317-325) and index.
Subject Added Entry-Topical Term
Machine learning. Machine learning --Mathematics. Geometry.
000 00000cam u2200205 a 4500
001 000046121287
005 20220714124836
008 220714s2022 si a b 001 0 eng d
020 ▼a 9789811660450 ▼q (hbk.)
035 ▼a (KERIS)BIB000016197895
040 ▼a 211044 ▼c 211044 ▼d 211009
082 0 4 ▼a 006.3/10151 ▼2 23
084 ▼a 006.310151 ▼2 DDCK
090 ▼a 006.310151 ▼b Y37g
100 1 ▼a 예종철.
245 1 0 ▼a Geometry of deep learning : ▼b a signal processing perspective / ▼c Jong Chul Ye.
260 ▼a Singapore : ▼b Springer, ▼c 2022.
300 ▼a xvi, 330 p. : ▼b col. ill. ; ▼c 24 cm.
490 1 ▼a Mathematics in industry ; ▼v 37.
504 ▼a Includes bibliographical references (p. 317-325) and index.
650 0 ▼a Machine learning.
650 0 ▼a Machine learning ▼x Mathematics.
650 0 ▼a Geometry.
830 0 ▼a Mathematics in industry ; ▼v 37.
900 1 ▼a Ye, Jong Chul.
945 ▼a ITMT

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.310151 Y37g Accession No. 121260389 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Part I Basic Tools for Machine Learning: 1. Mathematical Preliminaries.- 2. Linear and Kernel Classifiers.- 3. Linear, Logistic, and Kernel Regression.- 4. Reproducing Kernel Hilbert Space, Representer Theorem.- Part II Building Blocks of Deep Learning: 5. Biological Neural Networks.- 6. Artificial Neural Networks and Backpropagation.- 7. Convolutional Neural Networks.- 8. Graph Neural Networks.- 9. Normalization and Attention.- Part III Advanced Topics in Deep Learning.- 10. Geometry of Deep Neural Networks.- 11. Deep Learning Optimization.- 12. Generalization Capability of Deep Learning.- 13. Generative Models and Unsupervised Learning.- Summary and Outlook.- Bibliography.- Index.

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