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Applications of AI for Anomaly Detection

Whether your organization needs to monitor cybersecurity threats, fraudulent financial transactions, product defects, or equipment health, artificial intelligence (AI) can help catch data abnormalities before they impact your business. AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. These models can then be used to predict future anomalies.

With massive amounts of data available across industries and subtle distinctions between normal and abnormal patterns, it’s critical that organizations use AI to detect anomalies that pose a threat.

In this Deep Learning Institute (DLI) workshop, developers will learn how to implement multiple AI-based approaches to solve a specific use case: identifying network intrusions for telecommunications. They’ll learn three different anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

At the end of the workshop, developers will be able to use AI to detect anomalies in their work across telecommunications, cybersecurity, finance, manufacturing, and other key industries.

All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLIcertified instructor, and the opportunity to network with other developers, data scientists, and researchers attending the workshop. Attendees can earn a certificate to prove subject matter competency and support professional growth.

Technologies: NVIDIA RAPIDS™, XGBoost, TensorFlow, Keras, pandas, autoencoders, GANs, machine learning, artificial intelligence.

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