INTERNATIONAL STANDARD BOOK NUMBER
Qualification
(electronic bk.)
Qualification
(electronic bk.)
(Number (ISBN
1484251776
(Number (ISBN
9781484251775
Erroneous ISBN
9781484251768
INTERNATIONAL STANDARD MUSIC NUMBER
(Number (ISMN
10.1007/978-1-4842-5
(Number (ISMN
10.1007/978-1-4842-5177-5
NATIONAL BIBLIOGRAPHY NUMBER
Country Code
bnb
Number
15103
OTHER SYSTEM CONTROL NUMBERS
System Control Number
(OCoLC)1123175164
Cancelled or Invalid Control Number
(OCoLC)1126000339
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Beginning anomaly detection using Python-based deep learning :
Other Title Information
with Keras and PyTorch /
First Statement of Responsibility
Sridhar Alla, Suman Kalyan Adari.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York :
Name of Publisher, Distributor, etc.
Apress,
Date of Publication, Distribution, etc.
[2019]
Date of Publication, Distribution, etc.
�2019.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
416p.
Other Physical Details
illustrations.
GENERAL NOTES
Text of Note
Includes index.
NOTES PERTAINING TO BINDING AND AVAILABILITY
Text of Note
Available to OhioLINK libraries.
SUMMARY OR ABSTRACT
Text of Note
Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
OverDrive, Inc.
Stock Number
2CD5D0BD-3ABA-4D12-B7F0-63C761FAFA94
TOPICAL NAME USED AS SUBJECT
Entry Element
Anomaly detection (Computer security)
Entry Element
Python (Computer program language)
DEWEY DECIMAL CLASSIFICATION
Number
005
.
8
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
9
Book number
.
A25A45
2019
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Alla, Sridhar,
PERSONAL NAME - SECONDARY RESPONSIBILITY
Entry Element
Adari, Suman Kalyan,
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
Entry Element
Ohio Library and Information Network.
ORIGINATING SOURCE
Agency
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
Date of Transaction
20231007064335.2
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier
http://rave.ohiolink.edu/ebooks/ebc/9781484251775
Uniform Resource Identifier
http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-1-4842-5177-5
Uniform Resource Identifier
https://link.springer.com/10.1007/978-1-4842-5177-5
Uniform Resource Identifier
https://learning.oreilly.com/library/view/~/9781484251775/?ar
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270410
Y
