This chapter covers
- Convolutional neural networks for image classification
- TensorFlow and Keras — frameworks for building neural networks
- Using pre-trained neural networks
- Internals of a convolutional neural network
- Training a model with transfer learning
- Data augmentations — the process of generating more training data
Previously, we only have dealt with tabular data — data in CSV files. In this chapter, we’ll work with a completely different type of data — images.
The project we prepared for this chapter is classification of clothes. We will predict if an image of clothes is a T-shirt, a shirt, a skirt, a dress, or something else.
This is an image classification problem. To solve it, we will learn how to train a deep neural network for recognizing the types of clothes, and we’ll use TensorFlow and Keras for that. The materials of this chapter will help you start using neural networks and perform any similar image classification project.
Let’s start!
Imagine that we work at an online fashion marketplace. Our users upload thousands of images every day to sell their clothes. We want to help our users create listings faster by automatically recommending the right category for their clothes.