A Journey through Deep Learning

This course provides participants with both foundational knowledge and insights into cutting-edge developments.
Key features include:
• Fundamental principles of DL, including dense and convolutional networks.
• Advanced topics like transformers, and generative models.
• Exploration of practical challenges such as dataset bias and data scarcity.
• Poster sessions to foster collaboration and discussions.
Participants will also work on a group project to consolidate their learning and will be evaluated based on this final project.

The course unfolds over five days, starting with foundational concepts in machine learning and dense neural networks. It progresses to convolutional neural networks and their applications, before diving into sequential data processing using recurrent models and transformers. Advanced topics, including generative models and strategies to address challenges like dataset bias and data scarcity, are covered on the fourth day. The final day is dedicated to a collaborative group project, allowing participants to apply the knowledge gained throughout the course.

Attendees should have prior knowledge of linear algebra, probability, calculus, basic of machine learning and programming skills (familiarity with Python is an asset).