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self.sigmoid_derivative(activations[i]) delta.insert(0, delta_hidden) # Update weights and biases for i in range(self.num_hidden_layers + 1): self.weights[i] += learning_rate * np.dot(delta[i], activations[i].T) self.biases[i] += learning_rate * delta[i] def train(self, inputs, expected_outputs,