The Matrix Calculus You Need for Deep Learning

Brought to you by explained.ai The Matrix Calculus You Need For Deep Learning Terence Parr and Jeremy Howard (Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program. You might know Terence as the creator of the ANTLR parser generator. For more material, see Jeremy's fast.ai courses and University of San Francisco's Data Institute in-person version of the deep learning course.) Please send comments, suggestions, or fixes to Terence. Printable version (This HTML was generated from markup using bookish). A Chinese version is also available (content not verified by us). Abstract 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 ... Read full article.