I successfully completed this course with a 100.0% mark. Unlike the other two courses I had done as a part of this Deep Learning specialisation, there was much to learn for me in this one. I had only skimmed over a couple of papers on conv. nets in the past and hadn’t really implemented any aspects of this class of models except helping out colleagues in fixing bugs in their code. So I was stoked to do this course. And I was not disappointed. Andrew Ng designs and delivers his lectures very well and this course was no exception. The programming assignments and quizzes were engaging and moderately challenging. The idea of 1D, 2D and 3D convolutions was explained clearly and in sufficient depth in the lectures. They also covered some state-of-the-art convolutional architectures such as VGG Net, Inception Net, Network-in-Network and also applications such as Object and Face Recognition and Neural Style Transfer net, to all of which convolutional networks are a cornerstone. The reading list for the course was also very useful and interesting. All in all, a great resource in my opinion for someone interested in this topic! And as usual, here’s the certificate I received on completing this course.