LM101-083: Ch5: How to Use Calculus to Design Learning Machines

This particular podcast covers the material from Chapter 5 of my new book "Statistical Machine Learning: A unified framework" which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of both linear and nonlinear learning machines with lots of examples. We discuss how to use the matrix chain rule for deriving deep learning descent algorithms and how it is relevant to software implementations of deep learning algorithms. We also discuss how matrix Taylor series expansions are relevant to machine learning algorithm design and the analysis of generalization performance!! For additional details check out: and

You can listen to LM101-083: Ch5: How to Use Calculus to Design Learning Machines online on Radio and Podcast. Open the player on this page to stream the available audio.

LM101-083: Ch5: How to Use Calculus to Design Learning Machines is an episode from Learning Machines 101 by Richard M. Golden.

This episode is 34:22 long.

This episode was published on Aug 29, 2020.

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