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Beginning anomaly detection using python-based deep learning = implement anomaly detection applications with Keras and PyTorch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning anomaly detection using python-based deep learning/ by Suman Kalyan Adari, Sridhar Alla.
Reminder of title:	implement anomaly detection applications with Keras and PyTorch /
Author:	Adari, Suman Kalyan.
other author:	Alla, Sridhar.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xvi, 529 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Anomaly Detection -- Chapter 2: Introduction to Data Science -- Chapter 3: Introduction to Machine Learning -- Chapter 4: Traditional Machine Learning Algorithms. -Chapter 5: Introduction to Deep Learning -- Chapter 6: Autoencoders -- Chapter 7: Generative Adversarial Networks -- Chapter 8 Long Short-Term Memory Models -- Chapter 9: Temporal Convolutional Networks -- Chapter 10: Transformers -- Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection.
Contained By:	Springer Nature eBook
Subject:	Anomaly detection (Computer security) -
Online resource:	https://doi.org/10.1007/979-8-8688-0008-5
ISBN:	9798868800085

Beginning anomaly detection using python-based deep learning = implement anomaly detection applications with Keras and PyTorch /
Adari, Suman Kalyan.

Beginning anomaly detection using python-based deep learningimplement anomaly detection applications with Keras and PyTorch /[electronic resource] :by Suman Kalyan Adari, Sridhar Alla. - Second edition. - Berkeley, CA :Apress :2024. - xvi, 529 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Anomaly Detection -- Chapter 2: Introduction to Data Science -- Chapter 3: Introduction to Machine Learning -- Chapter 4: Traditional Machine Learning Algorithms. -Chapter 5: Introduction to Deep Learning -- Chapter 6: Autoencoders -- Chapter 7: Generative Adversarial Networks -- Chapter 8 Long Short-Term Memory Models -- Chapter 9: Temporal Convolutional Networks -- Chapter 10: Transformers -- Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection.

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. You will: Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.

ISBN: 9798868800085

Standard No.: 10.1007/979-8-8688-0008-5doiSubjects--Topical Terms:

3269771
Anomaly detection (Computer security)

LC Class. No.: TK5105.59

Dewey Class. No.: 005.8

Beginning anomaly detection using python-based deep learning = implement anomaly detection applications with Keras and PyTorch /
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245 1 0 $a Beginning anomaly detection using python-based deep learning $h [electronic resource] : $b implement anomaly detection applications with Keras and PyTorch / $c by Suman Kalyan Adari, Sridhar Alla.
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505 0 $a Chapter 1: Introduction to Anomaly Detection -- Chapter 2: Introduction to Data Science -- Chapter 3: Introduction to Machine Learning -- Chapter 4: Traditional Machine Learning Algorithms. -Chapter 5: Introduction to Deep Learning -- Chapter 6: Autoencoders -- Chapter 7: Generative Adversarial Networks -- Chapter 8 Long Short-Term Memory Models -- Chapter 9: Temporal Convolutional Networks -- Chapter 10: Transformers -- Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection.
520 $a This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. You will: Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.
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