Sections and Learning Objectives

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 2, Prediction Using Keras, introduces a dataset and problem that requires building a predictive model. In addition, this section has step by step code and outcome to help you create your first deep learning model using Keras. 
Section 3, Classifcation Using Keras, introduces you to how to build a classification model using Keras. Similar to Section 2, this section has step by step code and outcome.

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.