Hands-on machine learning with Scikit-Learn and TensorFlow : (Record no. 18192)

MARC details
000 -LEADER
fixed length control field 02185nam a2200229Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520102909.0
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 9781491962299
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q 325.5 .G47 2017
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Géron, Aurélien
Relator term author
245 #0 - TITLE STATEMENT
Title Hands-on machine learning with Scikit-Learn and TensorFlow :
Remainder of title concepts, tools, and techniques to build intelligent systems /
Statement of responsibility, etc. Aurélien Géron
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. c2017
300 ## - PHYSICAL DESCRIPTION
Extent xx, 547 pages :
Other physical details illustrations ;
Dimensions 24 cm.
365 ## - TRADE PRICE
Price amount USD33.29
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. Up and running with TensorFlow ; Introduction to artificial neural networks ; Training deep neural nets ; Distributing TensorFlow across devices and servers ; Convolutional neural networks ; Recurrent neural networks ; Autoencoders ; Reinforcement learning -- 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. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
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     Machine Learning LRC - Main National University - Manila General Circulation 09/03/2018 Purchased - Amazon 33.29   GC Q 325.5 .G47 2017 NULIB000015951 05/20/2025 c.1 05/20/2025 Books