Registration required at this link

MathWorks and PACE are partnering to offer two hands-on computing workshops, taught by MathWorks engineers, to PACE researchers and other members of the Georgia Tech community. 
In both sessions, you will also learn how to take advantage of PACE resources, which are available to all researchers at Georgia Tech (including a free tier available at no cost), to scale your MATLAB computations.

Artificial Intelligence techniques like deep learning are introducing automation to the products we build and the way we do business. These techniques can be used to solve complex problems related to images, signals, text and controls. Deep learning can achieve state-of-the-art accuracy in many human-like tasks, such as naming objects in a scene or recognizing optimal paths in an environment. The main tasks involved in deep learning are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.

In this hands-on lab, you will write code and use MATLAB® Online™ to:

• Train deep neural networks on GPUs in the cloud.
• Create deep learning models from scratch for image and signal data.
• Explore pretrained models and use transfer learning.
• Perform classification tasks on images and signals
• Explore deep learning applications such as ECG classification and pixel-level semantic segmentation on images

To participate in this workshop, you will need a MathWorks account (create MathWorks account).

Dr. Elvira Osuna-Highley is a Principal Customer Success Engineer at MathWorks. She partners with universities and research institutes to support teaching and research across Engineering and Science disciplines. Prior to joining MathWorks, Elvira was a Special Lecturer at Carnegie Mellon University in Computational Biology, a department in the School of Computer Science. Her courses focused on hands-on, experiential learning. Elvira earned a doctorate in Biomedical Engineering from Carnegie Mellon University where her research involved applying machine learning to fluorescence microscope images.

Who should attend?

Students, post docs and faculty at Georgia Tech that want to use machine learning in MATLAB and scale their computations to take advantage of PACE resources. 

Registration required at this link

MathWorks and PACE are partnering to offer two hands-on computing workshops, taught by MathWorks engineers, to PACE researchers and other members of the Georgia Tech community. 
In both sessions, you will also learn how to take advantage of PACE resources, which are available to all researchers at Georgia Tech (including a free tier available at no cost), to scale your MATLAB computations.

Who should attend?

Students, post docs and faculty at Georgia Tech that want to use parallel and GPU computing in MATLAB and scale their computations to take advantage of PACE resources. 

Registration closes one day prior to the workshop. Registrants will be sent the link to the virtual session by email on the morning of the workshop. If a session is no longer listed on the registration page, then it has reached capacity.

Please visit https://gatech.service-now.com/home?id=kb_article_view&sysparm_article=KB0042355 for more information and to register.

Registration: here

PACE has recently expanded containerized software workflows on Phoenix and Hive. Users are now able download, build, and run their own containers with Apptainer, a container runtime that is widely used in HPC centers. In this workshop, you will learn how to use Apptainer to:

• Easily download and run Docker images from DockerHub and other registries.
• Build new images using an Apptainer definition file, similarly to a Dockerfile.
• Learn tips and tricks for portable software workflows

This workshop will be taught virtually over Microsoft Teams. Please contact PACE support (pace-support@oit.gatech.edu) if you have any questions or concerns. We hope to see you there!