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020			_a9783030967567
024	7		_a10.1007/978-3-030-96756-7 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
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072		7	_aTEC008010 _2bisacsh
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090			_aQ325.5EBK
100	1		_aRafatirad, Setareh. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMachine Learning for Computer Scientists and Data Analysts _h[electronic resource] : _bFrom an Applied Perspective / _cby Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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. .
650		0	_aElectronic circuits.
650		0	_aCooperating objects (Computer systems).
650		0	_aMachine learning.
650	1	4	_aElectronic Circuits and Systems.
650	2	4	_aCyber-Physical Systems.
650	2	4	_aMachine Learning.
653		0	_aAlgorithms
700	1		_aHomayoun, Houman. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aChen, Zhiqian. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aPudukotai Dinakarrao, Sai Manoj. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-96756-7 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK