Course Description:
This workshop was offered as part of the learn_bAIome courses to learn about recurrent neural networks (RNNs) and their applications in biomedical signal processing. RNNs are vital tools in the field of neural networks, especially known for their capability to manage sequential data. This workshop provides an accessible introduction to RNNs, concentrating on their core concepts and various applications. We explored how RNNs excel at capturing temporal dependencies through their unique recurrent connections, making them highly effective for a variety of tasks.
Topics:
● Overview of RNN fundamentals and how they differ from other neural networks
● Key applications of RNNs in biomedical signal processing
● Reservoir computing (RC)
● Hands-on exercises and examples to illustrate RNN implementation and usage
Prerequisites:
A basic understanding of neural networks and machine learning concepts is expected as well as a familiarity with Python and basic programming skills.
Schedule
Day 1: Theoretical foundations of RNNs, including their architecture, key concepts, and differences from other neural networks.
Day 2: Hands-on session focused on building custom functions, classes, and modules to implement and extend RNN models.
Day 3: Practical exercises and model implementation.
Course materials: can be found here.