I'm currently enrolled in fastai's Deep Learning MOOC (version 3), and loving it so far. It's only been 2 lectures as of today, but folks are already building awesome stuff based on the content taught so far.
The course starts with the application of DL in Computer Vision, and in the very first lecture, course instructor Jeremy teaches us how to leverage transfer learning by making use of pre-trained ResNet models. I've been meaning to dive into the details of Resnets for a while, and this seems like a good time to do so.
This post is written in the vein of a summary-note, rather than that of a full-fledged introduction to resnets, ie, it's (sort-of) written for my own future reference, and can be helpful for somebody with some background on the topic.