It's the notes for a 40 min talk Rachel will give at the O'Reilly AI conference. It was originally meant to be a longer tutorial, so the scope had to be cut down significantly, whilst the title remained. There's lots of links in the notebook to additional resources with more background info.
Having said that, there really isn't much more linear algebra you need to implement neural networks from scratch. You'll need convolutions of course, although that's not too different from what's shown here.
For those interested in much more detail, Rachel has a full computational linear algebra course online http://www.fast.ai/2017/07/17/num-lin-alg/ . Most of that isn't needed for most deep learning, however.
Having said that, there really isn't much more linear algebra you need to implement neural networks from scratch. You'll need convolutions of course, although that's not too different from what's shown here.
For those interested in much more detail, Rachel has a full computational linear algebra course online http://www.fast.ai/2017/07/17/num-lin-alg/ . Most of that isn't needed for most deep learning, however.