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Machine Learning Theory and Applications (Record no. 67027)

MARC details
000 -LEADER
fixed length control field	04050nam a22003854u 4500
001 - CONTROL NUMBER
control field	10.1002/9781394220649
003 - CONTROL NUMBER IDENTIFIER
control field	Wiley
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20251030120309.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field	cr nn 008mam\|a
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	240112t2024\|\|\|\|xxu fs\| \|0\|\| 0\|eng\|\|
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781394220649
Qualifying information	electronic
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code	10.1002/9781394220649
Source of number or code	doi
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title	eng
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name	Vasques, Xavier
Relator code	aut
9 (RLIN)	91651
245 10 - TITLE STATEMENT
Title	Machine Learning Theory and Applications
Remainder of title	Hands‐on Use Cases with Python on Classical and Quantum Machines
Medium	[electronic resource]
Statement of responsibility, etc.	Xavier Vasques
250 ## - EDITION STATEMENT
Edition statement	1
260 ## - Date publicare
Name of publisher, distributor, etc.	Wiley
Date of publication, distribution, etc.	2024
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer	Wiley
Date of production, publication, distribution, manufacture, or copyright notice	2024
300 ## - PHYSICAL DESCRIPTION
Extent	512pp.;
336 ## - CONTENT TYPE
Content type term	text
Content type code	txt
Source	rdacontent
337 ## - MEDIA TYPE
Media type term	computer
Media type code	c
Source	rdamedia
338 ## - CARRIER TYPE
Carrier type term	online resource
Carrier type code	cr
Source	rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type	text file
Source	rdaft
347 ## - DIGITAL FILE CHARACTERISTICS
Encoding format	PDF
520 ## - SUMMARY, ETC.
Summary, etc.	Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN)	64754
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN)	91652
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN)	58441
653 00 - INDEX TERM--UNCONTROLLED
Uncontrolled term	machine learning; deep learning; generative AI; foundation models; quantum machine learning; data engineering; containerization; GPUs; CPUs; QPUs; mathematics; Python; Kubernetes; feature rescaling; AI Python; AI applications; MLOps
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information	Printed edition:
International Standard Book Number	9781394220618
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="https://doi.org/10.1002/9781394220649">https://doi.org/10.1002/9781394220649</a>
Host name	Wiley Online Library
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="https://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394220649">https://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394220649</a>
Host name	Wiley Online Library
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified	Cover Image
Uniform Resource Identifier	<a href="https://onlinelibrary.wiley.com/cms/asset/c2944473-5b48-4062-9192-6ac581abe93c/9781394220649.cover.gif">https://onlinelibrary.wiley.com/cms/asset/c2944473-5b48-4062-9192-6ac581abe93c/9781394220649.cover.gif</a>
Host name	Wiley Online Library

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