Course Overview
Learn to detect anomalies in large datasets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Certifications
This course is part of the following Certifications:
Prerequisites
- Professional data science experience using Python
- Experience training deep neural networks
Course Objectives
- 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
Follow On Courses
Outline: Applications of AI for Anomaly Detection (AAAD)
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
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