[Mageek]

Julia turns out to be a very nice language for problems like this. Many of the algorithms end up looking very much like the pseudocode from the original paper. We can use unicode as well, which makes a lot of the math (like gradient symbols) easier to read. We drop the type annotations to make it more legible. "Real" implementations could definitely improve what we have in terms of error checking and efficiency improvements, but we optimized for understanding and clarity.

Very often getting the figures to generate properly helped us catch mistakes in the code. I usually did this in a Jupyter notebook, because I can iterate there a lot faster than making a small in the book source and running the compilation process there.

I guess we didn't really have to get around the problem - the algorithm was of principle importance and then we had to figure out how to best produce figures for it.

[adamnemecek]

Type annotations make code more legible.