First off, I’d like to thank both Jeremy and Rachel for creating deep learning for coders. Jeremy is a great expositor. And his vision as to why you are doing this, mentioned in lesson 0 truly resonated with me. Problems that matter to many will be solved by the many. And I really believe you guys are on the right track implementing that inclusive vision, with play-first, theory-later approach.
OTOH, I have programming experience building various kinds of applications, on various host systems with various languages / paradigms etc. That part isn’t hard, so all the lessons that deal with Jupyter and AWS and sh etc, leave me glazed over. And I have preferred live editors and various other tools collected/created over the years which I am likely to use.
I imagine that there are others in a similar situation - who can write their own code, but are unfamiliar with deep learning and the attendant math. And find Jeremy’s talks clearer and more engaging than anything else currently available in blog or book.
Lesson 0 is great - just the conceptual bits - In fact it was just enough for me to go and stub out a sketch in code and it even worked (buggily, but it did). Are there conceptual resources you know of / can recommend that aren’t so broad that they cannot be implemented, but not highly coupled to implementation details like all the rest of the lessons? I know there isn’t more of it by Jeremy because I searched and came up empty but maybe I missed something. It would be a great help if he could do a talk or even a post in this direction.
- A human