Neural Network

This is brain-inspired AI model which uses interconnected nodes neurons in layers to finds patterns in input data, then learn from examples to make predictions
It contains an input layer, one or more hidden layers, and an output layer.

Neuron / y=wx+b

A neuron is the basic unit within a layer. It takes input, performs a computation, and produces an output. Each neuron has weight, bias, activation function
A neuron is a single computational unit (node) within a layer, taking inputs, multiplying by weights, adding a bias, and applying an activation function

input(x) is multipled with weight(w) and added to bais(b)

class Neuron:
    def __init__(self, num_inputs):
        self.weights = initialize_weights(num_inputs)
        self.bias = initialize_bias()
        self.activation_function = relu

    def forward(self, input_data):
        # Compute the weighted sum of inputs
        weighted_sum = sum(weight * input_value for weight, input_value in zip(self.weights, input_data)) + self.bias
        
        # Apply the activation function
        output = self.activation_function(weighted_sum)
        
        return output