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020 _a9783031037580
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024 7 _a10.1007/978-3-031-03758-0
_2doi
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072 7 _aTBC
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072 7 _aTEC000000
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100 1 _aShankar Shanthamallu, Uday.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987343
245 1 0 _aMachine and Deep Learning Algorithms and Applications
_h[electronic resource] /
_cby Uday Shankar Shanthamallu, Andreas Spanias.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXV, 107 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Signal Processing,
_x1932-1694
505 0 _aTable 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 _aThis 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.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_987344
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_987346
650 2 4 _aElectrical and Electronic Engineering.
_987349
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aSpanias, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987351
710 2 _aSpringerLink (Online service)
_987353
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031037689
776 0 8 _iPrinted edition:
_z9783031037481
776 0 8 _iPrinted edition:
_z9783031037788
830 0 _aSynthesis Lectures on Signal Processing,
_x1932-1694
_987356
856 4 0 _uhttps://doi.org/10.1007/978-3-031-03758-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c86085
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