國立虎尾科技大學 |

Machine Learning for Computer Scientists and Data Analysts = From an Applied Perspective /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning for Computer Scientists and Data Analysts/ by Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao.
其他題名:	From an Applied Perspective /
作者:	Rafatirad, Setareh.
其他作者:	Pudukotai Dinakarrao, Sai Manoj.
面頁冊數:	XV, 458 p. 157 illus., 140 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-96756-7
ISBN:	9783030967567

Machine Learning for Computer Scientists and Data Analysts = From an Applied Perspective /
Rafatirad, Setareh.

Machine Learning for Computer Scientists and Data AnalystsFrom an Applied Perspective /[electronic resource] :by Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao. - 1st ed. 2022. - XV, 458 p. 157 illus., 140 illus. in color.online resource.

Introduction -- 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.

This 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. .

ISBN: 9783030967567

Standard No.: 10.1007/978-3-030-96756-7doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: TK7867-7867.5

Dewey Class. No.: 621.3815

Machine Learning for Computer Scientists and Data Analysts = From an Applied Perspective /
LDR:03246nam a22003975i 4500 001 1088412
003 DE-He213
005 20220709152715.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030967567 $9 978-3-030-96756-7
024 7 $a 10.1007/978-3-030-96756-7 $2 doi
035 $a 978-3-030-96756-7
050 4 $a TK7867-7867.5
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 621.3815 $2 23
100 1 $a Rafatirad, Setareh. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1395599
245 1 0 $a Machine Learning for Computer Scientists and Data Analysts $h [electronic resource] : $b From an Applied Perspective / $c by Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XV, 458 p. 157 illus., 140 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction -- 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 $a This 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 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Cyber-Physical Systems. $3 1387591
650 1 4 $a Electronic Circuits and Systems. $3 1366689
650 0 $a Machine learning. $3 561253
650 0 $a Cooperating objects (Computer systems). $3 1387590
650 0 $a Electronic circuits. $3 563332
700 1 $a Pudukotai Dinakarrao, Sai Manoj. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1395602
700 1 $a Chen, Zhiqian. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1395601
700 1 $a Homayoun, Houman. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1395600
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030967550
776 0 8 $i Printed edition: $z 9783030967574
776 0 8 $i Printed edition: $z 9783030967581
856 4 0 $u https://doi.org/10.1007/978-3-030-96756-7
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)