Geometry of deep learning: a signal processing perspective

By:

Ye, Jong Chul

Series: Mathematics in Industry; 37Publication details: Springer, 2022. Singapore:Description: ix,330p.; hb; 25cmISBN:

9789811660450

Subject(s):

DDC classification:

006.31

Summary: Covers recent developments in deep learning and a wide spectrum of issues, with exercise problems for students Employs unified mathematical approaches with illustrative graphics to present various techniques and their results Closes the gap between the purely mathematical and implementation-oriented treatments of deep learning. https://link.springer.com/book/10.1007/978-981-16-6046-7

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	006.31 YEJ (Browse shelf(Opens below))	1	Checked out	16/12/2024	031182

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Previous	006.31 SHA Understanding machine learning: from theory to algorithms	006.31 WAT Machine learning refined: foundations, algorithms, and applications	006.31 WAT Algebraic geometry and statistical learning theory	006.31 YEJ Geometry of deep learning: a signal processing perspective	006.31 ZHE Feature engineering for machine learning: principles and techniques for data scientists	006.31015 MOI Algorithmic aspects of machine learning	006.310151 KNE Math for deep learning: what you need to know to understand neural networks	Next

includes bibliography and index

Covers recent developments in deep learning and a wide spectrum of issues, with exercise problems for students
Employs unified mathematical approaches with illustrative graphics to present various techniques and their results
Closes the gap between the purely mathematical and implementation-oriented treatments of deep learning.

https://link.springer.com/book/10.1007/978-981-16-6046-7

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