Machine and Deep Learning Algorithms and Applications (Record no. 86085)
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| 000 -LEADER | |
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| fixed length control field | 04925nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-03758-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730165056.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031037580 |
| -- | 978-3-031-03758-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 620 |
| 100 1# - AUTHOR NAME | |
| Author | Shankar Shanthamallu, Uday. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine and Deep Learning Algorithms and Applications |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 107 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Signal Processing, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Table of Contents:Preface -- Acknowledgments -- Introduction to Machine Learning -- Supervised Learning -- Unsupervised Learning -- Semi-Supervised Learning -- Neural Networks and Deep Learning -- Machine and Deep Learning Applications -- Conclusion and Future Directions -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Spanias, Andreas. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-03758-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2022. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal processing. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Technology and Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical and Electronic Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Speech and Image Processing. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1932-1694 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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