# Concepts of a Neural Network (NN)

simulation of human nervous system in its ability to learn and adpt.

## Units

1. Inputs
2. Weights
3. Transfer Function + net output
4. Activation Function
5. Outputs

### Flow

1. Update and Provide our Inputs;
2. Transfer Function;
3. Net OutPut (Inputs and Weights);
4. Actiation Function;
5. Calculate Output;

### Learnign

it is a process to Change Weights of inputs based on results ...
this happens to algorithm improve itself optimization ...

### Net

in this process Inputs and their Weights collecting together.

### Activation Function

Recieved Net Inputs (Weighted Inputs) and then Calculate output based on them.

## Multi Layer NN

1. Input Layer;
2. Hidden Layer;
3. Output Layer;

Hidden Layers recieved Data from all other layers at the end, then Populate as a Neuron.

### Input Data Normalization

a Process where all the input data normalized.
reduce data to an accepted ranges.
i.e. [0,1] or [-1, 1].

this peocess so important for us for making data more acceptable.

this can done by some standard form:

y = x - (x(min) * (d2 -d1)) / x(max - x(min)) + d1

x => value to normalized;
x(min)/x(max) => x Value range max and min;
d1,d2 => ranges to upper and lower normalization;

### Activation Functions

a function which calculate the output of a neuron.
it recieved a Net Input (Weighted functions);

1. Unit Step / Hard Threshold Functions;
2. Sigmoid Function;
3. Hyperbolic Tangent Function;

## Unti Step

a Graph by y axix Output and x axix Net Input and values in y between 0, 1 and x is 0 to TETA or threshold.

### Sigmoid

Change Activation Functions Shape;