LM101-083: Ch5: How to Use Calculus to Design Learning Machines 41g1m

This particular podcast covers the material from Chapter 5 of my new book “Statistical Machine...

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
 

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems +1 año 33:12 LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges +1 año 30:50 LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes +1 año 35:28 LM1010-082: Ch4: How to Analyze and Design Linear Machines +1 año 29:04 LM101-081: Ch3: How to Define Machine Learning (or at Least Try) +1 año 37:19 Ver más en APP Comentarios del episodio 6q6e6v