Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, Bìa mềm
Thể loại:Computers
Năm:2019
In lần thứ:1st ed.
Ngôn ngữ:english
Trang:427
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
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