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008			230325s2022 sz | s |||| 0|eng d
020			_a9783031037580
024	7		_a10.1007/978-3-031-03758-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.73 _b2022 EB
100	1		_aShankar Shanthamallu, Uday _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687547
245	1	0	_aMachine and Deep Learning Algorithms and Applications _cby Uday Shankar Shanthamallu, Andreas Spanias
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XV, 107 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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 to address 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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aSpanias, Andreas _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686104
776	0	8	_iPrinted edition: _z9783031037689
776	0	8	_iPrinted edition: _z9783031037481
776	0	8	_iPrinted edition: _z9783031037788
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-03758-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI