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Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch de Sridhar Alla,Suman Kalyan Adari
DescripciĆ³n - ReseƱa del editor 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 LearnUnderstand what anomaly detection is and why it is important in today's worldBecome familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-LearnKnow the basics of deep learning in Python using Keras and PyTorchBe aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and moreApply deep learning to semi-supervised and unsupervised anomaly detectionWho This Book Is ForData scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection Contraportada 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'll Learn:Understand what anomaly detection is and why it is important in today's worldBecome familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-LearnKnow the basics of deep learning in Python using Keras and PyTorchBe aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and moreApply deep learning to semi-supervised and unsupervised anomaly detection BiografĆa del autor Sridhar AllaĀ 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.Ā Suman Kalyan AdariĀ 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.
Detalles del Libro
- Name: Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch
- Autor: Sridhar Alla,Suman Kalyan Adari
- Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informƔticas
- TamaƱo del archivo: 15 MB
- Tipos de archivo: PDF Document
- Descargada: 435 times
- Idioma: EspaƱol
- Archivos de estado: AVAILABLE
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