Introduction

In the previous chapter, we learned how to create a Convolutional Neural Network (CNN) from scratch with Keras. We experimented with different architectures by adding more convolutional and Dense layers and changing the activation function. We compared the performance of each model by classifying images of cars and flowers into their respective classes and comparing their accuracies.

In real-world projects, however, you almost never code a convolutional neural network from scratch. You always tweak and train them as per the requirements. This chapter will introduce you to the important concepts of transfer learning and pre-trained networks (also known as pre-trained models), both of which are used in the industry.

We will use images and, rather than building a CNN from scratch, we will match these images on pre-trained models to try and classify them. We will also tweak our models to make them more flexible. The models we will use in this chapter are called VGG16...