Difference between revisions of "10-601 Neural networks and Deep Belief Networks"
From Cohen Courses
Jump to navigationJump to search| (2 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
| Line 7: | Line 7: | ||
=== Readings === | === Readings === | ||
| − | * Mitchell: Ch. 4, | + | * Mitchell: Ch. 4, Murphy Ch 16.5 |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Latest revision as of 16:44, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's slides: in Powerpoint, in PDF
Readings
- Mitchell: Ch. 4, Murphy Ch 16.5
What You Should Know Afterward
- What functions can be expressed with multi-layer networks that a single layer cannot express
- The backpropagation algorithm, and what loss is associated with it
- In outline, how deep neural networks are trained
