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
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
LM101-082: Ch4: How to Analyze and Design Linear Machines
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
LM101-080: Ch2: How to Represent Knowledge using Set Theory
LM101-079: Ch1: How to View Learning as Risk Minimization
LM101-078: Ch0: How to Become a Machine Learning Expert
LM101-077: How to Choose the Best Model using BIC
LM101-076: How to Choose the Best Model using AIC and GAIC
LM101-075: Can computers think? A Mathematician's Response (remix)
LM101-074: How to Represent Knowledge using Logical Rules (remix)
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
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