Requirements: .ePUB, .PDF reader, 22.0 MB
Overview: 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. 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. For data scientists and Machine Learning engineers of all levels of experience interested in learning the basics of Deep Learning applications in anomaly detection.
Genre: Non-Fiction > Tech & Devices
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