Applications of AI for Anomaly Detection Training

Whether your organization needs to monitor cybersecurity threats, fraudulent financial transactions, product defects, or equipment health, artificial intelligence 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 quickly detect anomalies that pose a threat.

In this workshop, you’ll learn how to implement multiple AI-based approaches to solve a specific use case of identifying network intrusions for telecommunications. You’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, you’ll be able to use AI to detect anomalies in your work across telecommunications, cybersecurity, finance, manufacturing, and other key industries.

Course Details


1 day


  • Professional data science experience using Python
  • Experience training deep neural networks

Skills Gained

  • Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs
  • Detect anomalies in datasets with both labeled and unlabeled data
  • Classify anomalies into multiple categories regardless of whether the original data was labeled
Course Outline
  • Introduction
  • Anomaly Detection in Network Data Using GPU-Accelerated XGBoost
    • Learn how to detect anomalies using supervised learning.
    • Prepare data for GPU acceleration using the provided dataset.
    • Train a binary and multi-class classifier using the popular machine learning algorithm XGBoost.
    • Assess and improve your model’s performance before deployment.
  • Anomaly Detection in Network Data Using GPU-Accelerated Autoencoder
    • Learn how to detect anomalies using modern unsupervised learning.
    • Build and train a deep learning-based autoencoder to work with unlabeled data.
    • Apply techniques to separate anomalies into multiple classes.
    • Explore other applications of GPU-accelerated autoencoders.
  • Project: Anomaly Detection in Network Data Using GANs
    • Learn how to detect anomalies using GANs.
    • Train an unsupervised learning model to create new data.
    • Use that new data to turn the problem into a supervised learning problem.
    • Compare the performance of this new approach to more established approaches.
  • Assessment and Q&A