Deep Learning, Linear Algebra, AI, Artificial Intelligence, Machine Learning.
self.output_size)) def forward(self, inputs): # Linear transformation and activation in the hidden layer hidden_input = np.dot(inputs, self.weights_input_hidden) + self.biases_hidden hidden_output = 1 / (1 + np.exp(-hidden_input)) # Sigmoid activation function # Linear transformation in the output