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Understanding Machine Learning:From theory to algorithms (Record no. 2479)

MARC details
000 -LEADER
fixed length control field 02606nam a22002177a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200224142453.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200118b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978-1-107-51282-5
028 ## - PUBLISHER NUMBER
Source Allied Informatics, Jaipur
Bill Number 7084
Bill Date 13/01/2020
Purchase Year 2019-20
040 ## - CATALOGING SOURCE
Original cataloging agency BSDU
Language of cataloging English
Transcribing agency BSDU
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number SHA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Shalev-Shwartz,Shai
245 ## - TITLE STATEMENT
Title Understanding Machine Learning:From theory to algorithms
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Delhi
Name of publisher, distributor, etc. Cambridge University Press
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent 397
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note DescriptionContentsResourcesCoursesAbout the Authors<br/>Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.<br/><br/>This book is written for the first year students of Engineering– A blend of theory and solved problems will equip the students with the fundamental knowledge and application of the coding concepts. This will nurture them to have a strong foundation for the courses in the subsequent semesters.<br/><br/>This book ensures a smooth and successful transition to being a skilled Python expert. The book uses a simple-to-complex and easy-to-learn approach throughout. The concept of ‘learning by-solving has been stressed everywhere in the book. Each feature of Python is treated in depth followed by a complete program example to illustrate its use. Wherever necessary, concepts are explained pictorially to facilitate better understanding. The book presents a contemporary approach to programming, offering a combination of theory and practice.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ben-David,Shai
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 Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     BSDU Knowledge Resource Center, Jaipur BSDU Knowledge Resource Center, Jaipur General Stacks 01/18/2020 995.00   006.31 SHA 018046 02/12/2020 995.00 01/18/2020 Books