Hands-on machine learning with scikit-learn, keras, and tensorflow : (Record no. 21032)

MARC details
000 -LEADER
fixed length control field 02227nam a2200241Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520103011.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250520s9999 xx 000 0 und d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781492032649
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q 325.5 .G47 2019
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Geron, Aurelien
Relator term author
245 #0 - TITLE STATEMENT
Title Hands-on machine learning with scikit-learn, keras, and tensorflow :
Remainder of title concepts, tools, and techniques to build intelligent systems /
Statement of responsibility, etc. Aurelien Geron
250 ## - EDITION STATEMENT
Edition statement 2nd Edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Sebastopol, California :
Name of publisher, distributor, etc. O'Reilly Media, Incorporated,
Date of publication, distribution, etc. c2019
300 ## - PHYSICAL DESCRIPTION
Extent xxv, 819 pages :
Other physical details color illustrations ;
Dimensions 24 cm
365 ## - TRADE PRICE
Price amount USD50
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note The fundamentals of machine learning -- The machine learning landscape -- End-to-end machine learning project -- Classification -- Training models -- Support vector machines -- Decision trees -- Ensemble learning and random forests -- Dimensionality reduction -- Neural networks and deep learning -- Introduction to artificial neural networks with Krekas -- Training deep neural networks -- Custom Models and training with TensorFlow -- Loading and Preprocessing data with Tensorflow -- Deep Computer vision using convolutional neutral networks -- Processing Sequences Using RNNs and CNNs -- Natural Language Processing with RNNs and Attention -- 17. Representation Learning and generative Learning using autoencoders and GANs -- 18. Reinforcement Learning -- 19. Training and deploying TensorFlow Models at scale. -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures
520 ## - SUMMARY, ETC.
Summary, etc. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     Gen. Ed. - CCIT LRC - Main National University - Manila General Circulation 12/03/2022 Purchased - Amazon 50.00   GC Q 325.5 .G47 2019 NULIB000018791 05/20/2025 c.1 05/20/2025 Books