At FAU MoD Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg, we were glad to have the free (online) two-day course on April 28-29th, 2022 about Deep Learning with MATLAB organized by MathWorks in cooperation with FAU MoD and CMAI (Fairfax, Virginia). It includes a short MATLAB introduction w.r.t. visualization and analyzation of Data.
Day 1: Introduction to MATLAB
When: Thursday, April 28, 2022, 15:00H to 16:30H (CEST)
Materials for the Introduction to MATLAB Session
In this session, you will learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your work. This session is targeted at those who are new to MATLAB. However, experienced MATLAB users may also benefit from the session, as the presenter, Evan Cosgrave (Ph.D.), will be covering some tips and tricks from the newer releases of MATLAB:
1. Accessing data from many sources (files, other software, hardware, etc.)
2. Using interactive tools for iterative exploration, design, and problem-solving
3. Automating and capturing your work in easy-to-write scripts and programs
4. Sharing your results with others by automatically creating reports
5. Building and deploying interactive applications
Day 2: Deep Learning with MATLAB – Hands-on Workshop
When: Friday, April 29, 2022, 15:00H to 17:00H (CEST)
Materials for the Deep Learning with MATLAB Session
Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries.
In this hands-on workshop, Ramnarayan Krishnamurthy (Ph.D.) and Kathi Kugler (Ph.D.) will introduce workflows for which you will write code in MATLAB Online to:
1. Train deep neural networks on GPUs in the cloud
2. Create deep learning models from scratch for image and signal data
3. Explore pre-trained models and use transfer learning
4. Learn how you can deploy your code to embedded targets
5. Discuss how you can interface with Python frameworks
*No installation of MATLAB is necessary.