  This paper is an attempt to explain all the matrix calculus you need in order
to understand the training of deep neural networks. We assume no math knowledge
beyond what you learned in calculus 1, and provide links to help you refresh
the necessary math where needed. Note that you do not need to understand this
material before you start learning to train and use deep learning in practice;
rather, this material is for those who are already familiar with the basics of
neural networks, and wish to deepen their understanding of the underlying math.
Don't worry if you get stuck at some point along the way---just go back and
reread the previous section, and try writing down and working through some
examples. And if you're still stuck, we're happy to answer your questions in
the Theory category at forums.fast.ai. Note: There is a reference section at
the end of the paper summarizing all the key matrix calculus rules and
terminology discussed here. See related articles at http://explained.ai
