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 #  AuthorTitleAccn#YearItem Type Claims
1 Borda, Monica Randomness and Elements of Decision Theory Applied to Signals I11928 2021 eBook  
2 Helias, Moritz Statistical Field Theory for Neural Networks I09583 2020 eBook  
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TitleRandomness and Elements of Decision Theory Applied to Signals
Author(s)Borda, Monica;Terebes, Romulus;Malutan, Raul;Ilea, Ioana;Cislariu, Mihaela;Miclea, Andreia;Barburiceanu, Stefania
PublicationCham, Springer International Publishing, 2021.
DescriptionXVII, 242 p. 254 illus., 168 illus. in color : online resource
Abstract NoteThis 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,Price9783030903145
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??
Item TypeeBook
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Accession#  Call#StatusIssued ToReturn Due On Physical Location
I11928     On Shelf    

2.    
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TitleStatistical Field Theory for Neural Networks
Author(s)Helias, Moritz;Dahmen, David
PublicationCham, Springer International Publishing, 2020.
DescriptionXVII, 203 p. 127 illus., 5 illus. in color : online resource
Abstract NoteThis 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,Price9783030464448
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
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09583     On Shelf    

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