Lecture 26: Structure of Neural Nets for Deep Learning

Description

This lecture is about the central structure of deep neural networks, which are a major force in machine learning. The aim is to find the function that’s constructed to learn the training data and then apply it to the test data.

Summary

The net has layers of nodes. Layer zero is the data.
We choose matrix of "weights" from layer to layer.
Nonlinear step at each layer! Negative values become zero!
We know correct class for the training data.
Weights optimized to (usually) output that correct class.

Related section in textbook: VII.1

Instructor: Prof. Gilbert Strang