Implementation

Such a model can be implemented really easily in Python (using NumPy for vector and matrix manipulations):

import numpy as np

class Neuron(object):
    """A simple feed-forward artificial neuron.
    Args:
        num_inputs (int): The input vector size / number of input values.
        activation_fn (callable): The activation function.
    Attributes:
        W (ndarray): The weight values for each input.
        b (float): The bias value, added to the weighted sum.
        activation_fn (callable): The activation function.
    """
    def __init__(self, num_inputs, activation_fn):
        super().__init__()
        # Randomly initializing the weight vector and bias value:
        self.W = np.random.rand(num_inputs)
        self.b = np.random.rand(1)
        self.activation_fn = activation_fn

    def forward(self, x):
        """Forward the input signal through the neuron."""
        z = np.dot(x, self.W) + self.b...