Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and Pytorch
暫譯: 使用基於 Python 的深度學習進行異常檢測入門：Keras 與 Pytorch 實作

Alla, Sridhar, Adari, Suman Kalyan

Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and Pytorch

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商品描述

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 of time 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 methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch.

What You Will Learn

  • Understand what anomaly detection is and why it is important in today's world
  • Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn
  • Know the basics of deep learning in Python using Keras and PyTorch
  • Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more
  • Apply deep learning to semi-supervised and unsupervised anomaly detection


Who This Book Is For
Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection

商品描述(中文翻譯)

利用這本易於理解的初學者指南，了解深度學習如何應用於異常檢測任務。這本書使用 Keras 和 PyTorch 的 Python，重點介紹各種深度學習模型如何應用於半監督式和無監督式的異常檢測任務。
本書首先解釋了什麼是異常檢測、其用途及其重要性。在介紹了使用 Scikit-Learn 的統計和傳統機器學習方法進行異常檢測後，接著提供了深度學習的介紹，詳細說明如何在 Keras 和 PyTorch 中構建和訓練深度學習模型，然後將重點轉向以下深度學習模型在異常檢測中的應用：各類型的自編碼器（Autoencoders）、限制玻爾茲曼機（Restricted Boltzmann Machines）、遞歸神經網絡（RNNs）與長短期記憶網絡（LSTMs）、以及時間卷積網絡（Temporal Convolutional Networks）。本書探討了無監督式和半監督式的異常檢測，以及基於時間序列的異常檢測基礎。
到本書結束時，您將對異常檢測的基本任務有透徹的理解，並掌握從傳統方法到深度學習的各種異常檢測方法。此外，您將了解 Scikit-Learn，並能在 Keras 和 PyTorch 中創建深度學習模型。

**您將學到什麼**

- 了解什麼是異常檢測以及為什麼在當今世界中它是重要的
- 熟悉使用 Scikit-Learn 進行異常檢測的統計和傳統機器學習方法
- 知道在 Python 中使用 Keras 和 PyTorch 的深度學習基礎
- 了解衡量模型性能的基本數據科學概念：理解 AUC 是什麼、精確度和召回率的含義等
- 將深度學習應用於半監督式和無監督式的異常檢測

**本書適合誰閱讀**
對學習深度學習在異常檢測應用基礎感興趣的數據科學家和機器學習工程師

作者簡介

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. He is a published author of books and an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. 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 will also present at Strata London in October 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie and daughter Evelyn. When he is not busy writing code, he loves to spend time with his family and also training, coaching, and organizing meetups.

Suman Kalyan Adari is an undergraduate student pursuing a BS degree in Computer Science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June 2019. He is quite passionate about deep learning, and specializes in its practical uses in various fields such as video processing, image recognition, anomaly detection, targeted adversarial attacks, and more.

作者簡介(中文翻譯)

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 AI 驅動的大數據解決方案和分析。他是多本書籍的出版作者，並在多個 Strata、Hadoop World、Spark Summit 及其他會議上積極演講。他在大型計算和分散式系統方面擁有多項專利，並已向美國專利商標局（US PTO）申請。他在多種技術上擁有豐富的實務經驗，包括 Spark、Flink、Hadoop、AWS、Azure、Tensorflow、Cassandra 等等。他於 2019 年 3 月在 Strata SFO 發表了關於使用深度學習進行異常檢測的演講，並將於 2019 年 10 月在 Strata London 進行演講。他出生於印度海得拉巴，現在與妻子 Rosie 和女兒 Evelyn 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，並進行訓練、指導和組織聚會。

Suman Kalyan Adari 是佛羅里達大學計算機科學學士學位的本科生。他自大一以來便在網絡安全領域進行深度學習研究，並於 2019 年 6 月在美國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關於可靠和安全機器學習的演講。他對深度學習充滿熱情，專注於其在視頻處理、圖像識別、異常檢測、針對性對抗攻擊等各個領域的實際應用。

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