Introduction

In the previous chapter, we learned how to create a convolutional neural network (CNN) from scratch with Keras. However, in real-world projects, you almost never code a convolutional neural network from scratch. You always tweak and train them as per the requirement. This book introduces you to the important concepts of transfer learning and pre-trained networks, also known as pre-trained models, which are used in the industry. This is an advanced level of machine learning, so this chapter assumes that you have adequate knowledge of neural networks and CNNs. We will use images and, rather than building a CNN from scratch, we will match these images on pre-trained models to try to classify them. We will also tweak our models to make them more flexible. The models we will use here are VGG16 and ResNet50, which we will discuss further in the chapter. Before starting to work on pre-trained models, we need to understand about transfer learning.