Introduction to Neural Networks

Neural networks are a more sophisticated version of feature crosses. In essence, neural networks learn the appropriate feature crosses for you.

Intro to Neural Networks

A Linear Model

Three blue circles in a row connected by arrows to a green circle above them

Add Complexity: Non-Linear?

Three blue circles in a row labeled "Input" connected by arrows to a row of yellow circles labeled "Hidden Layer" above them, which are in turn connected to a green circle labeled "Output" at the top.

More Complex: Non-Linear?

Three blue circles in a row labeled "Input" connected by arrows to a row of yellow circles labeled "Hidden Layer" above them, which are connected by arrows to a second "Hidden Layer" row of yellow circles, which are in turn connected to a green circle labeled "Output" at the top.

Adding a Non-Linearity

The same as the previous figure, except that a row of pink circles labeled 'Non-Linear Transformation Layer' has been added in between the two hidden layers.

Our Favorite Non-Linearity

A graph with slope of 0 and then linear once it passes x=0

Neural Nets Can Be Arbitrarily Complex

A complex neural network

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