國立高雄大學圖資館 |

Language: English

Back

Beginning anomaly detection using Py...

Adari, Suman Kalyan.

Beginning anomaly detection using Python-based deep learningwith Keras and PyTorch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning anomaly detection using Python-based deep learningby Sridhar Alla, Suman Kalyan Adari.
Reminder of title:	with Keras and PyTorch /
Author:	Alla, Sridhar.
other author:	Adari, Suman Kalyan.
Published:	Berkeley, CA :Apress :2019.
Description:	xvi, 416 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Anomaly detection (Computer security)
Online resource:	https://doi.org/10.1007/978-1-4842-5177-5
ISBN:	9781484251775$q(electronic bk.)

Beginning anomaly detection using Python-based deep learningwith Keras and PyTorch /
Alla, Sridhar.

Beginning anomaly detection using Python-based deep learningwith Keras and PyTorch /[electronic resource] :by Sridhar Alla, Suman Kalyan Adari. - Berkeley, CA :Apress :2019. - xvi, 416 p. :ill., digital ;24 cm.

Chapter 1: What is Anomaly Detection? -- Chapter 2: Traditional Methods of Anomaly Detection -- Chapter 3: Introduction to Deep Learning -- Chapter 4: Autoencoders -- Chapter 5: Boltzmann Machines -- Chapter 6: Long Short-Term Memory Models -- Chapter 7: Temporal Convolutional Network -- Chapter 8: Practical Use Cases of Anomaly Detection -- Appendix A: Introduction to Keras -- Appendix B: Introduction to PyTorch.

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. What You'll Learn: Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection.

ISBN: 9781484251775$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-5177-5doiSubjects--Topical Terms:

797123
Anomaly detection (Computer security)

LC Class. No.: QA76.9.A25 / A453 2019

Dewey Class. No.: 005.8

Beginning anomaly detection using Python-based deep learningwith Keras and PyTorch /
LDR:03296nmm a2200325 a 4500 001 568237
003 DE-He213
005 20200203112331.0
006 m d
007 cr nn 008maaau
008 200611s2019 cau s 0 eng d
020 $a 9781484251775$q(electronic bk.)
020 $a 9781484251768$q(paper)
024 7 $a 10.1007/978-1-4842-5177-5 $2 doi
035 $a 978-1-4842-5177-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25 $b A453 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b A416 2019
100 1 $a Alla, Sridhar. $3 853984
245 1 0 $a Beginning anomaly detection using Python-based deep learning $h [electronic resource] : $b with Keras and PyTorch / $c by Sridhar Alla, Suman Kalyan Adari.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xvi, 416 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: What is Anomaly Detection? -- Chapter 2: Traditional Methods of Anomaly Detection -- Chapter 3: Introduction to Deep Learning -- Chapter 4: Autoencoders -- Chapter 5: Boltzmann Machines -- Chapter 6: Long Short-Term Memory Models -- Chapter 7: Temporal Convolutional Network -- Chapter 8: Practical Use Cases of Anomaly Detection -- Appendix A: Introduction to Keras -- Appendix B: Introduction to PyTorch.
520 $a 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. What You'll Learn: Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection.
650 0 $a Anomaly detection (Computer security) $3 797123
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Python. $3 763308
650 2 4 $a Open Source. $3 758930
700 1 $a Adari, Suman Kalyan. $3 853985
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-5177-5
950 $a Professional and Applied Computing (Springer-12059)