[ML of Andrew Ng]Week 4 Neural Networks: Representation

Week 4 Neural Networks: Representation

Week 4 Neural Networks Representation
Non-linear hypotheses

Neurons and the brain
Neural Networks

The brain

Model representation
Neurons in the brain

Neural Network

Examples

Multi-class classification

Non-linear hypotheses

So, simple logistic regression together with adding in maybe the quadratic or the cubic features that’s just not a good way to learn complex nonlinear hypotheses when n is large because you just end up with too many features.

Neural Networks turns out to be a much better way to learn complex hypotheses, complex nonlinear hypotheses even when your input feature space, even when n is large.

Neurons and the brain

Neural Networks

Origins: Algorithms that try to mimic the brain.

Was very widely used in 80s and early 90s; popularity diminished in late 90s.

Recent resurgence: State-of-the-art technique for many applications

The brain

This is fascinating hypothesis that the way the brain does all of these different things is not worth like a thousand different programs, but instead, the way the brain does it is worth just a single learning algorithm.

Model representation

Neurons in the brain

Neural Network

Attention:x0=+1,call bias.

Examples

We put it together to make a three-layer neural network to get x1 XNOR x2

Multi-class classification

We use the index of max as predict.

In matlab:

[~,p] = max(all_p,[],2);