Preface

When I started reading papers about Deep Learning, typically published in conferences, I had hard time following them.

The key modeling details are presented in textual form, accompanied by architecture (block) diagrams. If the paper had any Math in them, they were mostly proofs of the key contributions but not for explaining the models in complete.

Despite this information, it was hard for me to understand them to be able to reproduce. I have to look at source code to see how they are implemented and go back to the paper and read again, and repeat this process. This was probably due to the my formal training Statistics. I always start with the model (expressed as equations). This unlearning took a long time. I am assuming that folks with training in Maths/ Applied Maths will also have hard time reading papers in the ML space for the same reason - there is no precision.

The intended audience is those with math background, that wants to appreciate modern deep learning models. We will not get into “why” deep learning works or their applications. In the resources section, I will provide ample references for those interested that wants to explore further.

Disclaimer

This course is by no means a replacement of any other resources available. Hopefully, the content and views presented complement the current literature and readers and students benefit from it.

openly,
The Saddle Point