I'd like to second karpathy here. When you do a practical interpretation of something like machine learning (and even deep learning!) I've had to cater to diffe

I'd like to second karpathy here. When you do a practical interpretation of something like machine learning (and even deep learning!) I've had to cater to different tastes. Usually people in these classes fall in to either the more engineering side where breaking down gradient descent can help, or mathy where they've already done convex optimization and know the trade offs of LBFGS vs Conjugate Gradient and all properties of parametric models are obvious. The best thing you can do here is work with the students 1 on 1 to fill in the gaps. There's no silver bullet. Which is why I'd say taking the class in person is always going to be better than notes. I think karpathy is hitting a wider audience with the way he's handling the notes though. Props to the way you're handling this!