Convolutional neural networks (CNNs) are popular deep neural networks and are considered to be the gold standard for large-scale image classification tasks. Applications involving CNNs include image recognition and classification, natural language processing, medical image classification, and many others. In this chapter, we will continue with supervised learning situations where a response variable exists. This chapter provides steps for applying image classification and recognition using convolutional neural networks with an easy-to-follow practical example involving fashion-related Modified National Institute of Standards and Technology (MNIST) data. We also make use of images of fashion items downloaded from the internet to explore the generalization potential of the classification model that we develop.

More specifically in this chapter, we cover the following topics:

• Data preparation
• Layers in convolutional neural networks
• Fitting the model
• Model evaluation and prediction
• Performance optimization tips and best practices
• Summary