### 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

## Key point

If network has $s_j$ units in layer j, $s_{j+1}$ units in layer j+1, then $\Theta^{(j)}$ will be of dimension $s_{j+1} \times (s_j + 1)$

This is very important to know or memorize later on for doing vectorized implementation. Or see pop quiz below.