Deep Learning Fundamentals using MATLAB Workshop

During this hands-on, lunch-and-learn workshop, a MathWorks engineer will introduce fundamental machine learning and deep learning concepts with “low code” MATLAB techniques. Through hands-on practice, you will learn how to import pre-trained deep-learning networks in MATLAB and retrain these networks (transfer learning) on different datasets. This workshop will also review steps to determine whether a machine-learning or deep-learning approach will best suit your data modeling needs as well as compute resources available to you through the university.

The workshop is open to all members of the UMD community interested in developing and performing ML and/or DL tasks using powerful tools.

Prerequisites

Some basic familiarity with MATLAB is assumed. If you are not familiar with MATLAB or want a refresher, you might wish to take the free self-paced MATLAB Onramp course from MathWorks (about 2 hours).

System Requirements

This is a bring-your-own-laptop style event. It is not required to have MATLAB installed on your laptop since we will use MATLAB Online for this workshop; use your UMD credentials to sign in.

If you want to have MATLAB, including the ML/Statistics and DL Toolboxes, installed on your laptop, you may download it at no charge on Terpware.

Resources

In case Terpware encounters issues, you may download a recent MATLAB version (e.g. R 2022b or R2023b) from the UMD MATLAB portal as hosted by MathWorks.

The difference between Mahine Learning and Deep Learning is briefly explained in this YouTube video.