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.

In this course, "End-to-End Natural Language Processing", you will learn about text data and how to process textual data using state-of-the-art AI tools. This course covers everything you need to know about natural language processing and various tasks. You will also learn about leveraging large language models for NLP, how to perform Prompt Engineering and Retrieval Augmented Generation, and how to create your own tiny AI models for specific tasks.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning natural language processing enjoyable and rewarding. You will also be able to tackle real-world problems in natural language processing. By the end of this course, you’ll have the skills and confidence to tackle any task with natural language processing.

This course is one of 6 courses in the Foundations in AI 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 End-to-End Natural Language Processing 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 AI and how to use PyTorch for a broad range of audiences.

In this course, "3D Vision – NeRFs and INRs", you will learn about the basics of 3D Vision and how to use state-of-the-art 3D vision algorithms such as Neural Radiance Fields, Gaussian Splats, and Implicit Neural Representations.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning 3D Vision enjoyable and rewarding. You will also be able to tackle real-world scenes for reconstruction and rendering. By the end of this course, you’ll have the skills and confidence to reconstruct any scenes from your smartphones and render them.

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 3D Vision - NeRFs & INRs course page from Iowa State Online.

Prerequisite

• Basic Python programming
• Basic understanding of deep learning and computer vision
• 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 3D Vision in Deep Learning and how to use these models for simulation, visualization, and, analysis.