Essentials of deep learning and AI : (Record no. 11066)

MARC details
000 -LEADER
fixed length control field 01970nam a22003017i 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250722115533.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250722b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789391030353
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GaU‬‬
Transcribing agency EG-GaU‬‬
Modifying agency EG-GaU‬‬
Description conventions rda
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 23
Classification number 006.3
Item number s.s.e.
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Soppin, Shashidhar,
Relator term author.
9 (RLIN) 60430
245 10 - TITLE STATEMENT
Title Essentials of deep learning and AI :
Remainder of title experience unsupervised learning, autoencoders, feature engineering, and time series analysis with TensorFlow, Keras, and scikit‑learn /
Statement of responsibility, etc. Shashidhar Soppin, Manjunath Ramachandra, B. N. Chandrashekar.
250 ## - EDITION STATEMENT
Edition statement 1st edition.
264 1# - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture New Delhi :
Name of producer, publisher, distributor, manufacturer BPB Publications,
Date of production, publication, distribution, manufacture, or copyright notice 2021.
300 ## - PHYSICAL DESCRIPTION
Extent 394 pages :
Other physical details illustrations ;
Dimensions 24 cm.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
Content type code txt
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
Media type code n
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
Carrier type code nc
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references.<br/>
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introduction ‡ 2. Supervised Machine Learning ‡ 3. System Analysis & Unsupervised Learning ‡ 4. Feature Engineering ‡ 5. Classification, Clustering, Association Rules & Regression ‡ 6. Time Series Analysis ‡ 7. Data Cleanup & Feature Selection ‡ 8. Ensemble Model Development ‡ 9. Design with Deep Learning ‡ 10. MLP Networks ‡ 11. LSTM Networks ‡ 12. Autoencoders ‡ 13. Applications ‡ 14. Emerging & Future Technologies.<br/>
520 ## - SUMMARY, ETC.
Summary, etc. A practical introduction to deep learning and AI emphasizing unsupervised learning techniques—including autoencoders, feature engineering, and time series analysis—using TensorFlow, Keras, and scikit‑learn. Ideal for data scientists and ML engineers. :contentReference[oaicite:1]{index=1}<br/>
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning$xStudy and teaching.
9 (RLIN) 60431
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Autoencoders (Computer science).
9 (RLIN) 60432
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ramachandra, Manjunath,
Relator term author.
9 (RLIN) 60433
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Chandrashekar, B. N.,
Relator term author.
9 (RLIN) 60434
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Media and mass communication Library Media and mass communication Library B4 08/19/2025   006.3 s.s.e MA0002510 08/19/2025 C.1 08/19/2025 Books