Hidden Layer Influence example with Simple Python

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# Input
X = np.array([1, 0])

# Hidden layer
W_hidden = np.array([[0.5, 0.4], [0.3, 0.7]])
b_hidden = np.array([-0.3, -0.1])
z_hidden = np.dot(X, W_hidden) + b_hidden
a_hidden = sigmoid(z_hidden)

# Output layer
W_output = np.array([0.6, 0.9])
b_output = -0.2
z_output = np.dot(a_hidden, W_output) + b_output
a_output = sigmoid(z_output)

print(f"Prediction (Happiness Probability): {a_output:.2f}")

Summary

Layer Role
Input Takes raw data
Hidden Learns non-obvious patterns (feature builders)
Output Combines those patterns into a final prediction

The hidden layer is where “understanding” happens.

Next – Hidden Layer Optimization Guide