32 min listen
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
ratings:
Length:
34 minutes
Released:
Aug 29, 2020
Format:
Podcast episode
Description
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: www.learningmachines101.com and www.statisticalmachinelearning.com
Released:
Aug 29, 2020
Format:
Podcast episode
Titles in the series (85)
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test): Episode Summary: This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. by Learning Machines 101