LEADER 03674nam 2200505 450 001 9910830558303321 005 20231102015751.0 010 $a9781394209101 035 $a(MiAaPQ)EBC30786527 035 $a(CKB)28495742000041 035 $a(Au-PeEL)EBL30786527 035 $a(OCoLC)1404054997 035 $a(OCoLC-P)1404054997 035 $a(CaSebORM)9781394209088 035 $a(EXLCZ)9928495742000041 100 $a20231102d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine and Deep Learning Using MATLAB $eAlgorithms and Tools for Scientists and Engineers /$fKamal I. M. Al-Malah 205 $aFirst edition. 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d[2024] 210 4$dİ2024 215 $a1 online resource (965 pages) 300 $aIncludes index. 311 $a9781394209088 330 $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. 606 $aMachine learning 606 $aNumerical analysis$xData processing 606 $aComputer programming 606 $aNumerical analysis$xComputer programs 615 0$aMachine learning. 615 0$aNumerical analysis$xData processing. 615 0$aComputer programming. 615 0$aNumerical analysis$xComputer programs. 676 $a001.642 700 $aAl-Malah$b Kamal I. M.$01646649 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910830558303321 996 $aMachine and Deep Learning Using MATLAB$93993757 997 $aUNINA