Picture 1 of 1
Stock photo
Picture 1 of 1
Stock photo
Beginning Anomaly Detection Using Python-Based Deep Learning : With Keras and Pytorch by Suman Kalyan Adari and Sridhar Alla (2019, Trade Paperback)
ThriftBooks (3917126)
98.9% positive Feedback
Price:
US $16.08
Approximately£11.77
+ $15.59 postage
Returns:
Condition:
Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and Pytorch by Alla, Sridhar; Adari, Suman Kalyan Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less
- Buy it nowBeginning Anomaly Detection Using Python-Based Deep Learning: With Keras and...
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherApress L. P.
ISBN-101484251768
ISBN-139781484251768
eBay Product ID (ePID)6038792810
Product Key Features
Number of Pages416 Pages
Publication NameBeginning Anomaly Detection Using Python-Based Deep Learning : with Keras and Pytorch
LanguageEnglish
SubjectIntelligence (Ai) & Semantics, Programming / Open Source, Programming Languages / Python
Publication Year2019
TypeTextbook
Subject AreaComputers
AuthorSuman Kalyan Adari, Sridhar Alla
FormatTrade Paperback
Dimensions
Item Height0.9 in
Item Weight26.5 Oz
Item Length10 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
IllustratedYes
SynopsisUtilize 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
Be the first to write a review
