First deep learning model
To build our first deep learning model in Python using Keras, we start the basic steps required for any machine learning model in Python. This includes importing required libraries, loading and cleaning the data, and splitting data into train and test. Then we define our neural network model by specifying input layer, hidden layers and output layer and their characteristics such as number of neurons in each layer and activation function. In addition to the usual machine learning steps, there is an extra step in Keras to compile the model. Compiling the model allows the underlying libraries such as TensorFlow to find the best way to represent the network for training and implementation on your hardware. In the sections, we explain each step, within the context of an example where we build a deep learning model to predict housing prices.