Deep Learning and XAI Techniques for Anomaly Detection: Integrate the theory and practice of deep anomaly explainability

Price: $46.99
(as of Dec 16,2024 08:23:29 UTC – Details)

Fix today. Protect forever. Secure your devices with the #1 malware removal and protection software

Publisher ‏ : ‎ Packt Publishing (January 31, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 218 pages
ISBN-10 ‏ : ‎ 180461775X
ISBN-13 ‏ : ‎ 978-1804617755
Item Weight ‏ : ‎ 13.7 ounces
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.46 inches

Fix today. Protect forever. Secure your devices with the #1 malware removal and protection software
Anomaly detection is a crucial task in various fields such as cybersecurity, finance, healthcare, and manufacturing. Traditional anomaly detection methods often struggle to handle complex and dynamic data patterns, leading to high false positive rates and missed anomalies. In recent years, deep learning and eXplainable Artificial Intelligence (XAI) techniques have shown promising results in improving anomaly detection performance.

Deep learning models, such as autoencoders, recurrent neural networks, and convolutional neural networks, have the ability to learn complex data representations and capture subtle anomalies in the data. These models can effectively detect anomalies in time series data, images, and text by learning from large amounts of training data. However, deep learning models are often considered as “black boxes” due to their complex architectures and non-linear transformations, making it challenging to interpret their decisions.

This is where XAI techniques come into play. XAI methods aim to provide insights into how deep learning models make predictions, enabling users to understand and trust the model’s decisions. Techniques such as saliency maps, LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and attention mechanisms can help explain the reasons behind the model’s anomaly detection results. By integrating XAI techniques with deep learning models, we can not only improve the detection accuracy but also enhance the model’s transparency and interpretability.

In this post, we will explore the theory and practice of deep learning and XAI techniques for anomaly detection. We will discuss the challenges of anomaly detection, the strengths of deep learning models, and the importance of explainability in anomaly detection tasks. We will also showcase some real-world applications of deep anomaly explainability and provide insights into how to effectively integrate deep learning and XAI techniques for anomaly detection. Stay tuned for an in-depth exploration of this exciting topic!
#Deep #Learning #XAI #Techniques #Anomaly #Detection #Integrate #theory #practice #deep #anomaly #explainability