I watched all of the 2018 Part 1 videos and it was not my style. One thing I liked is the positive attitudes of Jeremy and Rachel, but there was not much theory and a lot of time was spent on questions and answers for the participants of the lecture that I don't think necessary. I wanted to see definitions and "why", but the course spends too much time on "how". Sometimes I see "why" in the lecture, but many times it…
On the other hand, with the bottom-up approach of Andrew Ng in deeplearning.ai, you start with a lot of 'why', and later on get to 'how' (although in less detail and fewer best practices than we show). So it's good for people who want to understand the theory right away, and don't mind waiting a bit to understand how to use it.
A lot of our students did Andrew's course after ours, and many did it in the reverse order. All have reported finding the combination more helpful than either on their own. When we describe 'why' it's mainly with code, whereas with Andrew it's mainly with math - so which you prefer will also depend on which notation and framework you're more comfortable with.
(But I promise - you do get all the 'how' with us, particularly in part 2! Our students have gone on to OpenAI, Google Brain, and senior AI leadership positions at well known startups, as well as writing and implementing new papers. Here's an example of a student who just implemented a paper that was released within the last month: https://sgugger.github.io/deep-painterly-harmonization.html#... )