Detailed Course Outline
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