Items related to Beginning Anomaly Detection Using Python-Based Deep...
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
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.
From the Back Cover
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.
You will:
- 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
"About this title" may belong to another edition of this title.
- PublisherApress
- Publication date2024
- ISBN 13 9798868800078
- BindingPaperback
- LanguageEnglish
- Edition number2
- Number of pages545
Search results for Beginning Anomaly Detection Using Python-Based Deep...
Stock Image
Beginning Anomaly Detection Using Python-based Deep Learning : Implement Anomaly Detection Applications With Keras and Pytorch
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 46748383
Quantity: Over 20 available
Stock Image
Beginning Anomaly Detection Using Python-based Deep Learning : Implement Anomaly Detection Applications With Keras and Pytorch
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 46748383-n
Quantity: Over 20 available
Seller Image
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and Pytorch (Paperback or Softback)
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Seller rating 5 out of 5 stars
Paperback or Softback. Condition: New. Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and Pytorch 2.07. Book. Seller Inventory # BBS-9798868800078
Quantity: 5 available
Stock Image
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9798868800078
Quantity: Over 20 available
Stock Image
Beginning Anomaly Detection Using Python-based Deep Learning : Implement Anomaly Detection Applications With Keras and Pytorch
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 46748383-n
Quantity: Over 20 available
Stock Image
Beginning Anomaly Detection Using Python-Based Deep Learning (Paperback)
Published by
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2024
ISBN 13: 9798868800078
New
Paperback
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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 LearnUnderstand what anomaly detection is, why it it is important, and how it is appliedGrasp 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 PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is ForData scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection. Beg-Int user level Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798868800078
Quantity: 1 available
Stock Image
Beginning Anomaly Detection Using Python-based Deep Learning : Implement Anomaly Detection Applications With Keras and Pytorch
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 46748383
Quantity: Over 20 available
Stock Image
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9798868800078_new
Quantity: Over 20 available
Stock Image
Beginning Anomaly Detection Using Python-Based Deep Learning
Published by
Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, 2024
ISBN 13: 9798868800078
New
PAP
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9798868800078
Quantity: Over 20 available
Seller Image
Beginning Anomaly Detection Using Python-Based Deep Learning
Print on DemandSeller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 LearnUnderstand what anomaly detection is, why it it is important, and how it is appliedGrasp 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 PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applicationsWho This Book Is ForData scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection. 548 pp. Englisch. Seller Inventory # 9798868800078
Quantity: 2 available