Applications of AI for Anomaly Detection Training
Course Authors
Applications of AI for Anomaly Detection Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
With Applications of AI for Anomaly Detection Training, participants will 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).
This course is part of the following Certifications:
- NVIDIA-Certified Associate: Generative AI Multimodal
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 canceled, and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Applications of AI for Anomaly Detection Training with an expert instructor
- Applications of AI for Anomaly Detection Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Applications of AI for Anomaly Detection – https://www.wiley.com/
- Applications of AI for Anomaly Detection – https://www.packtpub.com/
- Applications of AI for Anomaly Detection – https://store.logicaloperations.com/
- Applications of AI for Anomaly Detection – https://us.artechhouse.com/
- Applications of AI for Anomaly Detection Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Applications of AI for Anomaly Detection Training course, participants can:
- 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
CUSTOMIZE IT
- We can adapt this Applications of AI for Anomaly Detection Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Applications of AI for Anomaly Detection course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Applications of AI for Anomaly Detection course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Applications of AI for Anomaly Detection course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Applications of AI for Anomaly Detection Training course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Applications of AI for Anomaly Detection Training course are:
- Professional data science experience using Python
- Experience training deep neural networks
COURSE SYLLABUS
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
