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Author | Title | Accn# | Year | Item Type | Claims |
| 1 |
Borda, Monica |
Randomness and Elements of Decision Theory Applied to Signals |
I11928 |
2021 |
eBook |
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| 2 |
Helias, Moritz |
Statistical Field Theory for Neural Networks |
I09583 |
2020 |
eBook |
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| Title | Randomness and Elements of Decision Theory Applied to Signals |
| Author(s) | Borda, Monica;Terebes, Romulus;Malutan, Raul;Ilea, Ioana;Cislariu, Mihaela;Miclea, Andreia;Barburiceanu, Stefania |
| Publication | Cham, Springer International Publishing, 2021. |
| Description | XVII, 242 p. 254 illus., 168 illus. in color : online resource |
| Abstract Note | This book offers an overview on the main modern important topics in random variables, random processes, and decision theory for solving real-world problems. After an introduction to concepts of statistics and signals, the book introduces many essential applications to signal processing like denoising, texture classification, histogram equalization, deep learning, or feature extraction. The book uses MATLAB algorithms to demonstrate the implementation of the theory to real systems. This makes the contents of the book relevant to students and professionals who need a quick introduction but practical introduction how to deal with random signals and processes |
| ISBN,Price | 9783030903145 |
| Keyword(s) | 1. Computer science???Mathematics
2. EBOOK
3. EBOOK - SPRINGER
4. MATHEMATICAL STATISTICS
5. OPERATIONS RESEARCH
6. Operations Research and Decision Theory
7. Probability and Statistics in Computer Science
8. SIGNAL PROCESSING
9. Signal, Speech and Image Processing
10. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
11. Statistics??
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| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I11928 |
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On Shelf |
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2.
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| Title | Statistical Field Theory for Neural Networks |
| Author(s) | Helias, Moritz;Dahmen, David |
| Publication | Cham, Springer International Publishing, 2020. |
| Description | XVII, 203 p. 127 illus., 5 illus. in color : online resource |
| Abstract Note | This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra |
| ISBN,Price | 9783030464448 |
| Keyword(s) | 1. EBOOK
2. EBOOK - SPRINGER
3. MACHINE LEARNING
4. Mathematical Models of Cognitive Processes and Neural Networks
5. MATHEMATICAL STATISTICS
6. Neural networks (Computer science)??
7. Neurosciences
8. Probability and Statistics in Computer Science
9. STATISTICAL PHYSICS
10. Statistical Physics and Dynamical Systems
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| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09583 |
|
|
On Shelf |
|
|
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