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.


Learning Objectives

By participating in this workshop, you’ll:
  • 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

Download workshop datasheet (PDF 81.7 KB)

Workshop Outline

(15 mins)
  • Meet the instructor.
  • Create an account at courses.nvidia.com/join
Anomaly Detection in Network Data Using GPU-Accelerated XGBoost
(120 mins)
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.
Break (60 mins)
Anomaly Detection in Network Data Using GPU-Accelerated Autoencoder
(120 mins)
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.
Break (15 mins)
Project: Anomaly Detection in Network Data Using GANs
(120 mins)
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 (15 mins)

Workshop Details

Duration: 8 hours

Price: Price: $500 for public workshops, contact us for enterprise workshops.  


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

Suggested materials to satisfy prerequisites: Getting Started with Deep Learning, Intro to Machine Learning

Technologies: NVIDIA RAPIDS, XGBoost, TensorFlow, Keras, pandas, autoencoders, GANs

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Language: English

Upcoming Workshops

Upcoming Public Workshops

North America / Latin America

Wednesday, September 22, 2021
9:00 a.m.–5:00 p.m. PDT

Europe / Middle East / Africa

Wednesday, September 22, 2021
9:00 a.m.–5:00 p.m. CEST

If your organization is interested in boosting and developing key skills in AI, accelerated data science, or accelerated computing, you can request instructor-led training from the NVIDIA DLI.