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Deep Learning Architectures: A Mathematical Approach by Ovidiu Calin (English) P
US $83.60
ApproximatelyRM 365.33
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Located in: Fairfield, Ohio, United States
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eBay item number:364686942254
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN-13
- 9783030367237
- Book Title
- Deep Learning Architectures
- ISBN
- 9783030367237
- Subject Area
- Mathematics, Computers
- Publication Name
- Deep Learning Architectures : a Mathematical Approach
- Publisher
- Springer International Publishing A&G
- Item Length
- 10 in
- Subject
- Intelligence (Ai) & Semantics, Probability & Statistics / General, Applied
- Publication Year
- 2021
- Series
- Springer Series in the Data Sciences Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Weight
- 52.2 Oz
- Item Width
- 7 in
- Number of Pages
- Xxx, 760 Pages
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3030367231
ISBN-13
9783030367237
eBay Product ID (ePID)
23050429659
Product Key Features
Number of Pages
Xxx, 760 Pages
Publication Name
Deep Learning Architectures : a Mathematical Approach
Language
English
Publication Year
2021
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, Applied
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Series in the Data Sciences Ser.
Format
Trade Paperback
Dimensions
Item Weight
52.2 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Reviews
"This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view." (T. C. Mohan, zbMATH 1441.68001, 2020)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
Introductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.
Synopsis
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
LC Classification Number
QA76.9.M35
Item description from the seller
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eBay item number:364686942254
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