000			05066nam a22005655i 4500
001			979-8-8688-0008-5
003			DE-He213
005			20251030110905.0
007			cr nn 008mamaa
008			240101s2024 xxu| s |||| 0|eng d
020			_a9798868800085 _9979-8-8688-0008-5
024	7		_a10.1007/979-8-8688-0008-5 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
100	1		_aAdari, Suman Kalyan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _968788
245	1	0	_aBeginning Anomaly Detection Using Python-Based Deep Learning _h[electronic resource] : _bImplement Anomaly Detection Applications with Keras and PyTorch / _cby Suman Kalyan Adari, Sridhar Alla.
250			_a2nd ed. 2024.
264		1	_aBerkeley, CA : _bApress : _bImprint: Apress, _c2024.
300			_aXVI, 529 p. 582 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 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			_aThis 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.
650		0	_aArtificial intelligence. _958441
650		0	_aMachine learning. _955059
650		0	_aPython (Computer program language). _964608
650		0	_aOpen source software. _99313
650	1	4	_aArtificial Intelligence. _958441
650	2	4	_aMachine Learning. _955059
650	2	4	_aPython. _947553
650	2	4	_aOpen Source. _966214
700	1		_aAlla, Sridhar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _968789
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9798868800078
776	0	8	_iPrinted edition: _z9798868800092
776	0	8	_iPrinted edition: _z9798868806049
856	4	0	_uhttps://doi.org/10.1007/979-8-8688-0008-5
912			_aZDB-2-CWD
912			_aZDB-2-SXPC
999			_c56972 _d56972