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GPU Acceleration for Machine Learning and AI Essentials
The "GPU Acceleration for Machine Learning and AI Essentials" course explains how to optimize ML models using GPU hardware. Learn to accelerate training, optimize memory usage, and deploy large-scale models in real-time environments. As ML models are increasingly deployed in security-sensitive areas like facial recognition and anomaly detection, securing the underlying GPU infrastructure is vital. This course discusses how to protect GPU pipelines from tampering, leakage, and adversarial attacks. You’ll learn both performance and security tuning to support trustworthy AI deployments.
Course Overview
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Learning Objectives
- GPU Acceleration for Machine Learning and AI Essentials
- Understand how GPUs accelerate machine learning and artificial intelligence workloads.
- Learn about the key libraries and frameworks that leverage GPU acceleration for AI.
- Explore techniques for optimizing machine learning models for GPU execution.
- Identify the benefits of using GPUs for training and inference in AI applications.
- Recognize the role of GPU acceleration in enhancing the speed and efficiency of AI-powered cybersecurity tools for threat detection and analysis.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.