| 期刊全称 | Beginning Anomaly Detection Using Python-Based Deep Learning | | 期刊简称 | Implement Anomaly De | | 影响因子2023 | Suman Kalyan Adari,Sridhar Alla | | 视频video | | | 发行地址 | Explains the machine learning workflow, from data processing through interpretation of model performance.Focuses on time-series with models like LSTM and‘TCN..Covers generative modeling via GANs and s | | 图书封面 |  | | 影响因子 | .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 ( | | Pindex | Book 2024Latest edition |
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发表于 2025-3-21 18:15:39
