Parallelism in Deep Learning

In this course, "Parallelism in Deep Learning", you will learn about the need for parallelism in deep learning and how to use different methods of parallelism in deep learning. You will also learn about leveraging data parallelism and model parallelism workflows for your AI models on HPC infrastructures.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning parallelism in deep learning enjoyable and rewarding. You will also be able to tackle real-world model training problems on HPC clusters. By the end of this course, you'll have the skills and confidence to train your AI models at scale using multiple GPUs and nodes.

This course is one of 6 courses in the Advanced AI Techniques pilot Micro-Credential pathway offered by the Translational AI Center at Iowa State University.

For more information regarding the course including: Learning Outcomes, Assessments, and a Course Outline please visit the Parallelism in Deep Learning course page from Iowa State Online.

Prerequisite

Basic Python programming
Basic understanding of deep learning
Basic PyTorch programming

Intended Audience
The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a basic understanding of high-performance computing for deep learning and how to use these models for a broad range of audiences.

Pages / Length:
Publication Date: 12/2024

Format

Price

Canvas eCourse

$500.00

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Permanent link for this product: https://store.extension.iastate.edu/product/17181