Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorch
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- Nombre de pages416
- PrésentationBroché
- FormatGrand Format
- Poids0.804 kg
- Dimensions18,0 cm × 25,1 cm × 2,5 cm
- ISBN978-1-4842-5176-8
- EAN9781484251768
- Date de parution11/10/2019
- ÉditeurApress
Résumé
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 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.
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 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.