A simple neural network - example of a simple perceptron using a logistic (sigmoid) activation function for binary classification - in 12 lines of Python3.11 code

nn.py

from numpy import array, dot, exp, random

training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = array([[0, 1, 1, 0]]).T
rng = random.default_rng(1)
synaptic_weights = 2 * rng.random((3, 1)) - 1

for _ in range(10000):
    output = 1 / (1 + exp((dot(-training_set_inputs, synaptic_weights))))
    synaptic_weights += dot(training_set_inputs.T, (training_set_outputs - output) * output * (1 - output))

print(1 / (1 + exp((dot(-array([1, 0, 0]), synaptic_weights)))))