Representation Learning
Please start by reading the full Deep Learning Book - Chapter 7: Regularization for Deep Learning.
The chapter is quite long as it covers a large variety of commonly applied regularization techniques that can of course also be combined. Here are some points to guide you during reading:
Primary objective: You want to get an overview of possible options.
What is the rationale behind the different approaches?
What are typical application scenarios?
Take note of possible advantages and disadvantage as well as potential pitfalls!
Think about, whether and how the described techniques could be applied in the context of the course project!
Post your remarks, insights and open questions in your blog and in the forum! Help each other to make sure you are not getting stuck too long with some unresolved issue.
Deadline for questions to be considered in class is November 26, 7am. I will also try to accommodate things that come in later but I cannot make guarantees. The earlier you bring up questions, the better.
This week, there is no mandatory further reading. However, you are invited to explore the vast deep learning literature and post your findings in your blog and the dedicated forum thread.
Right now, many highly interesting deep representation learning papers are being published …
Please use the course wiki and the forum to post and discuss ideas on how to start working on the course project and to coordinate activities!