**TABLE OF CONTENTS**

# intro to neural networks

## definitions

*features:*

*weights:*

*activation function:*

- we add positive features and subtract negative features multiplied by their weight
- have to pick a normalization function too (i.e. sigmoid, etc)

*neuron:*

- takes the inputs and multiplies them by their weights
- sums them up
- applies the activation function to the sum

## types

### feedforward

- data passes through the different input nodes till it reaches the output node
- moves in only one direction from the first tier onwards until it reaches the output node
- this is also known as a front propagated wave which is usually achieved by using a classifying activation function

- the sum of the products of the inputs and their weights are calculated

### radial basis

- distance of any point relative to the centre
- 2 layers
- inner: the features are combined with the radial basis function
- output of these features is taken into account when calculating the same output in the next time-step