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Machine learning for computer scientists and data analystsfrom an applied perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for computer scientists and data analystsby Setareh Rafatirad ... [et al.].
其他題名:	from an applied perspective /
作者:	Rafatirad, Setareh.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xv, 458 p. :ill. (chiefly color), digital ;24 cm.
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$q(electronic bk.)

Machine learning for computer scientists and data analystsfrom an applied perspective /
Rafatirad, Setareh.

Machine learning for computer scientists and data analystsfrom an applied perspective /[electronic resource] :by Setareh Rafatirad ... [et al.]. - Cham :Springer International Publishing :2022. - xv, 458 p. :ill. (chiefly color), digital ;24 cm.

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$q(electronic bk.)

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

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for computer scientists and data analystsfrom an applied perspective /
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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.
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