Explained, Deep Learning, Hyperparameter, AI, Backpropagation.
return 1 / (1 + np.exp(-x)) def sigmoid_derivative(x): return x * (1 - x) # Initialize parameters as some random variables w, v, b = 0.5, 0.3, -0.2 X = 2 # Input Y = 0.8 # Desireed output learning_rate = 0.01 # Let's make the math equation z = v * X + b a = sigmoid(z) Y_pred = w * a # Compute error