Neural networks are a collection of individuals nodes called as artificial neurons or **perceptrons**.

Each perceptron takes in an input array, multiples it with weights array and add a bias value to create a computed sum.

This computed computed is passed to an activation function to compute the final output value of a neuron.

We can see the basic parts of a perceptron below:

We can calculate the summation value of neuron inputs, weight and bias in the for a sample neuron as explained in example below:

```
#Example of dot product using bumpy
import numpy as np
#Sample input to perception
inputs = [1.2, 2.2, 3.3, 2.5]
#Weights passed to perception
weights = [0.4,0.6,-0.7, 1.1]
#bias for a particular perception
bias = 2
#Take dot product between weights and input
#and add bias to the summation value
output = np.dot(weights, inputs) + biasprint(output)
#Output:-4.24
```

Here **np.dot** function is used to calculate dot product between the input and the weights.

Internally it works as follows:

The output value from above function is fed to an activation function to calculate final value of a perceptron. I will cover various activation functions and their working in another article since it is a vast concept requiring it's own article.

That's it for today's short tutorial on dot product using bumpy.

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Reference:Neural Networks from Scratch by SentDex

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## Discussion