Notes of fast.ai's course Practical Deep Learning.
This is something I am currently reading through, so these notes will be particularly messy.
I have currently read up to ch. 1, "Let's move on to something much less sexy, but perhaps significantly more widely commercially useful: building models from plain tabular data.".
Links
Other Courses
Quotes
Might categorise these later
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?
Practical Deep Learning, ch. 1
Overfitting is the single most important and challenging issue when training for all machine learning practitioners, and all algorithms
Practical Deep Learning, ch. 1