Beginning Anomaly Detection Using Python-Based Deep Learning 2nd ed

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.

What You Will Learn

• 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

Who This Book Is For

Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.

這本針對初學者的書籍將幫助您通過學習尖端的機器學習和深度學習技術來理解和執行異常檢測。這本更新的第二版專注於監督、半監督和無監督的異常檢測方法。在本書的過程中，您將學習如何在實際應用中使用Keras和PyTorch。它還引入了關於GAN和transformer的新章節，以反映深度學習的最新趨勢。

《使用基於Python的深度學習進行異常檢測入門》首先介紹了異常檢測的概念、重要性和應用。然後介紹了核心數據科學和機器學習建模概念，然後深入介紹了使用scikit-learn進行異常檢測的傳統機器學習算法，如OC-SVM和Isolation Forest。接下來，作者解釋了機器學習和深度學習的基本知識，以及如何在Keras和PyTorch中實現多層感知器進行監督異常檢測。從這裡開始，重點轉向使用深度學習模型進行異常檢測的應用，包括各種類型的自編碼器、循環神經網絡（通過LSTM）、時間卷積網絡和transformer，後三種架構應用於時間序列異常檢測。這版新增了關於GAN（生成對抗網絡）的章節，以及在時間序列異常檢測背景下涵蓋transformer架構的新材料。

閱讀完本書後，您將全面了解異常檢測以及在各種情境下處理異常檢測的方法，包括時間序列數據。此外，您還將獲得對scikit-learn、GAN、transformer、Keras和PyTorch的介紹，使您能夠創建自己的基於機器學習或深度學習的異常檢測器。

《本書的學習目標》
- 理解異常檢測的概念、重要性和應用
- 掌握機器學習的核心概念
- 掌握使用scikit-learn進行傳統機器學習方法進行異常檢測
- 了解如何在Python中使用Keras和PyTorch進行深度學習
- 通過pandas處理數據，使用F1-score、精確度和召回率等指標評估模型的性能
- 將深度學習應用於監督、半監督和無監督的異常檢測任務，包括表格數據和時間序列應用

《本書適合對象》
對深度學習在異常檢測中的基礎應用感興趣的數據科學家和機器學習工程師，無論經驗水平如何。

Suman Kalyan Adari is a machine learning research engineer. He obtained a B.S. in Computer Science at the University of Florida and a M.S. in Computer Science specializing in Machine Learning at Columbia University. He has been conducting deep learning research in adversarial machine learning since his freshman year at the University of Florida and presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon in June 2019. Currently, he works on various anomaly detection tasks spanning behavioral tracking and geospatial trajectory modeling.

He is passionate about deep learning, and specializes in various fields ranging from video processing, generative modeling, object tracking, time-series modeling, and more.

Sridhar Alla is the co-founder and CTO of Bluewhale, which helps organizations big and small in building AI-driven big data solutions and analytics, as well as SAS2PY, a powerful tool to automate migration of SAS workloads to Python-based environments using Pandas or PySpark. He is a published author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March 2019 and also presented at Strata London in October 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie, his daughters Evelyn andMadelyn, and his son, Jayson. When he is not busy writing code, he loves to spend time with his family. He also enjoys training, coaching, and organizing meetups.

Suman Kalyan Adari是一位機器學習研究工程師。他在佛羅里達大學獲得了計算機科學學士學位，並在哥倫比亞大學獲得了計算機科學碩士學位，專攻機器學習。自從他在佛羅里達大學的大一時開始，他一直在進行對抗性機器學習的深度學習研究，並在2019年6月在俄勒岡州波特蘭舉行的IEEE可靠系統和網絡研討會上發表了演講。目前，他致力於各種異常檢測任務，包括行為跟踪和地理空間軌跡建模。

他對深度學習充滿熱情，專攻於各個領域，包括視頻處理、生成建模、物體跟踪、時間序列建模等等。

Sridhar Alla是Bluewhale的聯合創始人和首席技術官，該公司幫助各種規模的組織構建基於人工智慧的大數據解決方案和分析，同時還開發了SAS2PY，一個強大的工具，可以使用Pandas或PySpark自動將SAS工作負載遷移到基於Python的環境中。他是一位發表過作品的作者，並在許多會議上擔任熱情的演講者，包括Strata、Hadoop World和Spark Summit。他還在美國專利商標局申請了幾項關於大規模計算和分佈式系統的專利。他在多個技術領域擁有豐富的實踐經驗，包括Spark、Flink、Hadoop、AWS、Azure、Tensorflow、Cassandra等等。他在2019年3月的Strata SFO上發表了有關使用深度學習進行異常檢測的演講，並在2019年10月的Strata London上進行了演講。他出生於印度海得拉巴，現居美國新澤西州，與妻子Rosie、女兒Evelyn和Madelyn以及兒子Jayson一起生活。當他不忙於編寫代碼時，他喜歡與家人共度時光。他還喜歡培訓、指導和組織聚會。