Hands-on unsupervised learning using Python : (Record no. 1540)
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control field | 21726553 |
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control field | 20220114143430.0 |
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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 |
Source | bnb |
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 |
Modifying agency | BDX |
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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 | |
Content type term | text |
Content type code | txt |
Source | rdacontent |
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Media type term | unmediated |
Media type code | n |
Source | rdamedia |
338 ## - CARRIER TYPE | |
Carrier type term | volume |
Carrier type code | nc |
Source | rdacarrier |
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. |
Source of term | lcgft |
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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 |
No items available.