000			03753nam a22003854u 4500
001			10.1002/9781394209118
003			Wiley
005			20251030120310.0
007			cr nn 008mam|a
008			231020t2023||||xxu fs| |0|| 0|eng||
020			_a9781394209118 _qelectronic
024	7		_a10.1002/9781394209118 _2doi
041			_aeng
100	1		_aAl‐Malah, Kamal _4aut _991724
245	1	0	_aMachine and Deep Learning Using MATLAB _bAlgorithms and Tools for Scientists and Engineers _h[electronic resource] _cKamal Al‐Malah
250			_a1
260			_bWiley _c2023
264		1	_bWiley _c2023
300			_a592pp.;
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_aMACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps Retraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.
650		4	_aComputer-Aided Engineering _966003
650		4	_aArtificial Intelligence _958441
650		4	_aComputational / Numerical Methods
653	0	0	_aImage acquisition; image analysis; neural networks; training model accuracy; model loss; and RMSE; image labeling; object identification; regression classification; text recognition; matlab tools; matlab apps; matlab engineering
776	0	8	_iPrinted edition: _z9781394209088
856	4	0	_uhttps://doi.org/10.1002/9781394209118 _aWiley Online Library
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394209118 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/a99ddb66-5548-449c-8592-6cf2276cb360/9781394209118.cover.gif _aWiley Online Library
999			_c67060 _d67060