Beginning Anomaly Detection Using Python-Based Deep Learning, 2/e (Paperback)
暫譯: 使用Python深度學習入門異常檢測，第2版 (平裝本)
Adari, Suman Kalyan, Alla, Sridhar
- 出版商: Apress
- 出版日期: 2024-01-02
- 售價: $1,980
- 貴賓價: 9.5 折 $1,881
- 語言: 英文
- 頁數: 527
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798868800078
- ISBN-13: 9798868800078
-
相關分類:
DeepLearning、Python、程式語言
立即出貨 (庫存=1)
相關主題
商品描述
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.
商品描述(中文翻譯)
這本以初學者為導向的書籍將幫助您理解並執行異常檢測，透過學習尖端的機器學習和深度學習技術。這本更新的第二版專注於監督式、半監督式和非監督式的異常檢測方法。在書中，您將學習如何在實際應用中使用 Keras 和 PyTorch。它還介紹了有關 GANs（生成對抗網絡）和 transformers 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》首先介紹異常檢測、其重要性及應用。接著涵蓋核心數據科學和機器學習建模概念，然後深入探討傳統的機器學習算法，如 OC-SVM 和 Isolation Forest，使用 scikit-learn 進行異常檢測。隨後，作者解釋機器學習和深度學習的基本要素，以及如何在 Keras 和 PyTorch 中實現多層感知器以進行監督式異常檢測。接下來，重點轉向深度學習模型在異常檢測中的應用，包括各類自編碼器、循環神經網絡（透過 LSTM）、時間卷積網絡和 transformers，後三種架構應用於時間序列異常檢測。本版新增了一章關於 GANs（生成對抗網絡），以及涵蓋時間序列異常檢測背景下的 transformer 架構的新材料。
完成本書後，您將對異常檢測有透徹的理解，並掌握在各種情境下（包括時間序列數據）處理異常檢測的方法。此外，您將對 scikit-learn、GANs、transformers、Keras 和 PyTorch 有初步的了解，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。
您將學到的內容：
- 理解什麼是異常檢測，為什麼它重要，以及如何應用
- 掌握機器學習的核心概念
- 精通使用 scikit-learn 進行異常檢測的傳統機器學習方法
- 理解如何使用 Keras 和 PyTorch 在 Python 中進行深度學習
- 通過 pandas 處理數據，並使用 F1-score、精確度和召回率等指標評估模型性能
- 將深度學習應用於監督式、半監督式和非監督式異常檢測任務，適用於表格數據集和時間序列應用
本書適合對象：
對於所有經驗水平的數據科學家和機器學習工程師，對學習深度學習在異常檢測中的應用基礎感興趣。
作者簡介
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.
作者簡介(中文翻譯)
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計算機科學學士學位，並在哥倫比亞大學獲得專攻機器學習的計算機科學碩士學位。他自佛羅里達大學大一時期開始進行對抗性機器學習的深度學習研究，並於2019年6月在俄勒岡州波特蘭舉行的IEEE可靠系統與網絡研討會上發表了相關研究。目前，他專注於各種異常檢測任務，包括行為追蹤和地理空間軌跡建模。
他對深度學習充滿熱情，專精於多個領域，包括視頻處理、生成建模、物體追蹤、時間序列建模等。
Sridhar Alla 是 Bluewhale 的共同創辦人及首席技術官，該公司幫助大小型組織建立以人工智慧驅動的大數據解決方案和分析，並且開發了 SAS2PY，這是一個強大的工具，用於自動將 SAS 工作負載遷移到基於 Python 的環境中，使用 Pandas 或 PySpark。他是一位已出版的作者，並在多個會議上積極發表演講，包括 Strata、Hadoop World 和 Spark Summit。他在大型計算和分散式系統方面擁有多項專利，並在多種技術上擁有豐富的實務經驗，包括 Spark、Flink、Hadoop、AWS、Azure、TensorFlow、Cassandra 等。他於2019年3月在 Strata SFO 發表了「使用深度學習進行異常檢測」的演講，並於2019年10月在 Strata London 進行了演示。他出生於印度海得拉巴，現在與妻子 Rosie、女兒 Evelyn 和 Madelyn 以及兒子 Jayson 一起住在美國新澤西州。當他不忙於編寫程式碼時，他喜歡與家人共度時光，也喜歡訓練、指導和組織聚會。
