Machine Learning – Stanford -Week 4 – Neural Networks: Representation
The following content from edX course Machine Learning taught by Andrew Ng.
Introduction to Neural Networks
Welcome to week 4! This week, we are covering neural networks. Neural networks is a model inspired by how the brain works. It was very widely used in 80s and early 90s; popularity diminished in late 90s (computationally expensive).
Recent resurgence: State-of-the-art technique for many applications. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
Why we need a new algorithm?
Feature “explosion” for logistic regression. Neural network will hopefully help solving this issue.
The “one learning algorithm” hypothesis
How it can solve complex non-linear problem
If network has units in layer j, units in layer j+1, then will be of dimension
This is very important to know or memorize later on for doing vectorized implementation. Or see pop quiz below.
Non-linear classification example
Multiple output units: One-vs-all