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
LM101-006: How to Interpret Turing Test Results
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
LM101-004: Can computers think? A mathematician.s response
LM101-003: How to Represent Knowledge using Logical Rules
LM101-002: How to Build a Machine that Learns to Play Checkers
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