Sections in this chapter

Section 1, Getting Started with Deep Learning, discusses the general framework for a deep learning project. In this section, we will learn how to systematically approach a deep learning problem. In addition, this section discusses the similarities and differences between building a deep learning and machine learning algorithm.
Section 3, Housing Price Prediction Using Keras, introduces the data and problem we are investigating in addition to step by step code and outcome to create your first deep learning model using Keras. 

Getting started with deep learning

Building a deep learning model using Keras involves the general steps required for building any other machine learning model including loading the data, splitting data into train and test sets, and defining, fitting and evaluating the model. In Keras, there is an extra step after defining the model called compiling the model. We require this step so that Keras can use the backend libraries such as TensorFlow. The backend library then chooses the best way to represent the network for training and making predictions using your hardware such as CPU, GPU or even distributed.
Since compiling the model allows for the backend libraries to decide on how to train the network, you need to define training parameters in this step. Training the network means finding the best set of weights, such that the model can make accurate predictions. The training parameters to be defined include the cost function used to evaluate the weights, and the optimizer used to search through possible weights and choose the best (optimal) weights. In addition, there are several optional metrics which you can use to collect and report during training.

House Price Prediction Using Keras

We will walk you through a hands-on example to apply deep learning on a real-world data set. For this hands-on exercise, we will be using Jupyter notebooks, Keras and TensorFlow.
Unlike most deep learning tutorials, we won’t be using handwritten digits classification from MNIST. We will be using a real estate data to walk you through an applied example in deep learning.

House Sales in King County, USA