[jph00]

Top-down vs bottom-up depends to some extent on what you enjoy, vs what you need to be patient about. With our top-down approach you do get to all of the 'why', but only after understanding 'how'. So it's good for people who want to get started doing stuff right away, and don't mind waiting a bit to understand the details. It means you can start experimenting and building a good intuition for training models, which I believe is the most important skill for a practitioner.

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#... )