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008			220709s2022 sz a o |||| 0|eng d
020			_a9783030967567
024	7		_a10.1007/978-3-030-96756-7 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aRafatirad, Setareh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685522
245	1	0	_aMachine Learning for Computer Scientists and Data Analysts : _bFrom an Applied Perspective _cby Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XV, 458 páginas) _b157 ilustraciones, 140 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- Metadata Extraction and Data Preprocessing -- Data Exploration -- Practice Exercises -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Model Evaluation and Optimization -- ML in Computer vision - autonomous driving and object recognition -- ML in Health-care - ECG and EEG analysis -- ML in Embedded Systems - resource management -- ML for Security (Malware) -- ML in Big-data Analytics -- ML in Recommender Systems -- ML for Ontology Acquisition from Text and Image Data -- Adversarial Learning -- Graph Adversarial Neural Networks -- Graph Convolutional Networks -- Hardware for Machine Learning -- Software Frameworks.
520			_aThis textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications. Describes traditional as well as advanced machine learning algorithms; Enables students to learn which algorithm is most appropriate for the data being handled; Includes numerous, practical case-studies; implementation codes in Python available for readers; Uses examples and exercises to reinforce concepts introduced and develop skills.
988			_aSpringer_Engineering_2022
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aHomayoun, Houman _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685523
700	1		_aChen, Zhiqian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685524
700	1		_aDinakarrao, Sai Manoj Pudukotai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685525
776	0	8	_iPrinted edition: _z9783030967550
776	0	8	_iPrinted edition: _z9783030967574
776	0	8	_iPrinted edition: _z9783030967581
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-96756-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b12/2022 _dz _eb _zSI