For a long time now, I’ve been planning to start learning about deep learning, but whenever I start reading about it, either through books such as The MIT Press Deep Learning book or through some online video or MOOCs, I get bombarded with strange mathematical formulas and complex equations. Of course, this complex mathematics is reasonably necessary to understand Deep Learning models but it also repels a beginner from learning about this field, as a beginner is more result oriented.

If I was a Ph.D. student and I was researching to make a specific algorithm perform better in some way, sure I’d have to learn about all those equations and formulas, but the reality is, I’m not, for now. I am just a curious student, who’d like to get a general overview of how Deep Learning works and build some apps to test different models on real-world data.

But for a long time, most of the resources available to learn about Deep Learning were focused on those Ph.D. level students, quite boring and technical, with little to no practical projects whatsoever. So, whenever I tried starting this endeavor to learn about Deep Learning, just after a day or two, I’d get frustrated and abandon it altogether.

You can’t imagine my surprise when I learned about this course, fast.ai, taught by Jeremy Howard. The approach that they are using to teach Deep Learning is quite opposite of what’s being used elsewhere. Instead of diving deeper into the technicalities from the start, they are using a top-down code-first approach where they let the student experiment with different algorithms and techniques by implementing them to solve real-world problems, like from Kaggle competitions, etc, from the first lecture.

This is what I’ve been looking for all along. A way to ease into the Deep Learning field, experiment with different models, and, when I get comfortable, delve deeper into mathematics.

The main objective of the teaching strategy being followed by Jeremy in this course seems quite interesting and attractive but let’s see how effective it is and how well it is executed.

I’m hopeful that this will be an amazing experience for me, I’ll learn a lot and practice a lot. I’ll keep blogging about my experience taking this course, the things I liked, the things I didn’t, the problems I faced, and the solutions to those. Let’s see how it turns out.