
Probability for Deep Learning Quantum
A Many-Sorted Algebra View
$501.12
- Paperback
362 pages
- Release Date
21 January 2025
Summary
Probability for Deep Learning
This book serves as a comprehensive guide to the fundamental concepts of probability theory and its applications in deep learning. It delves into the mathematical underpinnings of probabilistic models and explores how they are leveraged to build and understand complex neural networks.
Key topics covered include:
- Foundational Probability: Basic probability rules, random variables, probability distri…
Book Details
| ISBN-13: | 9780443248344 |
|---|---|
| ISBN-10: | 0443248346 |
| Author: | Charles R. Giardina |
| Publisher: | Elsevier Science & Technology |
| Imprint: | Morgan Kaufmann Publishers In |
| Format: | Paperback |
| Number of Pages: | 362 |
| Release Date: | 21 January 2025 |
| Weight: | 750g |
| Dimensions: | 235mm x 191mm |
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About The Author
Charles R. Giardina
Charles R. Giardina was born in the Bronx, NY, on December 29, 1942. He received the B.S. degree in mathematics from Fairleigh Dickinson University, Rutherford, NJ, and the M.S. degree in mathematics from Carnegie Institute of Technology, Pittsburgh, PA. He also received the M.E.E. degree in 1969, and the Ph.D. degree in mathematics and electrical engineering in 1970 from Stevens Institute of Technology, Hoboken, NJ. Dr. Giardina was Professor of Mathematics, Electrical Engineering, and Computer Science at Fairleigh Dickinson University from 1965 to 1982. From 1982 to 1986, he was a Professor at the Stevens Institute of Technology. From 1986 to 1996, he was a Professor at the College of Staten Island, City University of New York. From 1996, he was with Bell Telephone Laboratories, Whippany, NJ, USA. His research interests include digital signal and image processing, pattern recognition, artificial intelligence, and the constructive theory of functions. Dr. Giardina has authored numerous papers in these areas, and several books including, Mathematical Models for Artificial Intelligence and Autonomous Systems, Prentice Hall; Matrix Structure Image Processing, Prentice Hall; Parallel Digital Signal Processing: A Unified Signal Algebra Approach, Regency; Morphological Methods in Image and Signal Processing, Prentice Hall; Image Processing – Continuous to Discrete: Geometric, Transform, and Statistical Methods, Prentice Hall; and A Unified Signal Algebra Approach to Two-Dimensional Parallel Digital Signal Processing, Chapman and Hall/CRC Press.
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