عنوان

Beginning anomaly detection using Python-based deep learning :

ويژگيها

(electronic bk.)

ويژگيها

(electronic bk.)

شابک

1484251776

شابک

9781484251775

شابک اشتباه

9781484251768

شماره ISMN

10.1007/978-1-4842-5

شماره ISMN

10.1007/978-1-4842-5177-5

کد کشور

bnb

شماره

15103

شماره کنترل سيستم

(OCoLC)1123175164

شماره کنترل سيستم غير معتبر يا حذف شده

(OCoLC)1126000339

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Beginning anomaly detection using Python-based deep learning :

ساير اطلاعات عنواني

with Keras and PyTorch /

نام نخستين پديدآور

Sridhar Alla, Suman Kalyan Adari.

محل نشرو پخش و غیره

New York :

نام ناشر، پخش کننده و غيره

Apress,

تاریخ نشرو بخش و غیره

[2019]

تاریخ نشرو بخش و غیره

�2019.

نام خاص و کميت اثر

416p.

ساير جزييات

illustrations.

متن يادداشت

Includes index.

متن يادداشت

Available to OhioLINK libraries.

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

OverDrive, Inc.

شماره انبار

2CD5D0BD-3ABA-4D12-B7F0-63C761FAFA94

عنصر شناسه ای

Anomaly detection (Computer security)

عنصر شناسه ای

Python (Computer program language)

شماره

ويراست

شماره رده

نشانه اثر

عنصر شناسه اي

Alla, Sridhar,

عنصر شناسه اي

Adari, Suman Kalyan,

عنصر شناسه اي

Ohio Library and Information Network.

سازمان

کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه

تاريخ عمليات

20231007064335.2

قواعد فهرست نويسي ( بخش توصيفي )

pn

مشخصه منبع قراردادي

http://rave.ohiolink.edu/ebooks/ebc/9781484251775

مشخصه منبع قراردادي

http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-1-4842-5177-5

مشخصه منبع قراردادي

https://link.springer.com/10.1007/978-1-4842-5177-5

مشخصه منبع قراردادي

https://learning.oreilly.com/library/view/~/9781484251775/?ar

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource (off-campus)

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource

نوع ماده

BL

کد کاربرگه

270410

تكميل شده

Y