| 期刊全稱 | Beginning Anomaly Detection Using Python-Based Deep Learning | | 期刊簡(jiǎn)稱 | With Keras and PyTor | | 影響因子2023 | Sridhar‘Alla,Suman Kalyan Adari | | 視頻video | http://file.papertrans.cn/183/182228/182228.mp4 | | 發(fā)行地址 | Explains some of the most effective and efficient anomaly detection methods available.Provides annotated Python code snippets and notebooks.Covers the most contemporary approaches to anomaly detection | | 圖書(shū)封面 |  | | 影響因子 | 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 oftime 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 m | | Pindex | Book 20191st edition |
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