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 ...
Read More
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
Read Less
Add this copy of Beginning Anomaly Detection Using Python-Based Deep to cart. $5.97, good condition, Sold by ThriftBooks-Atlanta rated 5.0 out of 5 stars, ships from Brownstown, MI, UNITED STATES, published 2019 by Apress.
Add this copy of Beginning Anomaly Detection Using Python-Based Deep to cart. $5.97, good condition, Sold by ThriftBooks-Dallas rated 4.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 2019 by Apress.
Add this copy of Beginning Anomaly Detection Using Python-Based Deep to cart. $5.99, like new condition, Sold by St. Vinnie's Books rated 5.0 out of 5 stars, ships from Eugene, OR, UNITED STATES, published 2019 by Apress.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Like New. Paperback 100% of proceeds go to charity! Clean copy with no writing, notes, creases or highlighting. Item may have been opened and read, but signs of use are minimal.
Add this copy of Beginning Anomaly Detection Using Python-Based Deep to cart. $10.54, like new condition, Sold by Academic Book Solutions rated 5.0 out of 5 stars, ships from Medford, NY, UNITED STATES, published 2019 by Apress.
Add this copy of Beginning Anomaly Detection Using Python-Based Deep to cart. $36.94, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 2019 by Apress.
