In deep learning, it really helps if you have the motivation to fix your model to get it to do better.
That’s when you start learning the relevant theory.
But you need to have the model in the first place.
The hardest part of deep learning is artisanal:
- how do you know if you’ve got enough data,
- whether it is in the right format,
- if your model is training properly, and, if it’s not, what you should do about it?
What you will need to do to succeed, however, is to apply what you learn in this book to a personal project, and always persevere.
Focus on your hobbies and passions—setting yourself four or five little projects rather than striving to solve a big, grand problem tends to work better when you’re getting started.