Hands-on unsupervised learning using Python : (Record no. 1540)

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control field 21726553
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005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220114143430.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200923t20192019caua b 001 0 eng c
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2020304238
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
National bibliography number GBB955721
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016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 019326633
Source Uk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781492035640
Qualifying information (paperback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1492035645
Qualifying information (paperback)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)on1066070019
040 ## - CATALOGING SOURCE
Original cataloging agency YDX
Language of cataloging eng
Transcribing agency YDX
Description conventions rda
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041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
042 ## - AUTHENTICATION CODE
Authentication code pcc
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.73.P98
Item number P38 2019
080 ## - УДК
Universal Decimal Classification number 004.4
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Patel, Ankur A.,
Relator term author.
245 10 - TITLE STATEMENT
Title Hands-on unsupervised learning using Python :
Remainder of title how to build applied machine learning solutions from unlabeled data /
Statement of responsibility, etc. Ankur A. Patel.
250 ## - EDITION STATEMENT
Edition statement First edition.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Sebastopol, CA :
Name of producer, publisher, distributor, manufacturer O'Reilly Media,
Date of production, publication, distribution, manufacture, or copyright notice 2019.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice ©2019
300 ## - PHYSICAL DESCRIPTION
Extent xx, 337 pages :
Other physical details illustrations ;
Dimensions 24 cm.
336 ## - CONTENT TYPE
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504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Part 1. Fundamentals of unsupervised learning. Unsupervised learning in the machine learning ecosystem -- End-to-end machine learning project -- Part 2. Unsupervised learning using Scikit-learn. Dimensionality reduction -- Anomaly detection -- Clustering -- Group segmentation -- Part 3. Unsupervised learning using TensorFlow and Keras. Autoencoders -- Hands-on autoencoder -- Semisupervised learning -- Part 4. Deep unsupervised learning using TensorFlow and Keras. Recommender systems using restricted Boltzmann machines -- Feature detection using deep belief networks -- Generative adversarial networks -- Time series clustering -- Conclusion.
520 ## - SUMMARY, ETC.
Summary, etc. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Python (Computer program language)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst00817247
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst01004795
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Python (Computer program language)
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst01084736
655 #7 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Handbooks and manuals.
Source of term fast
Authority record control number or standard number (OCoLC)fst01423877
655 #7 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Handbooks and manuals.
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Универсальная десятичная классификация
Koha item type Electronic edition
Suppress in OPAC No

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