A convolutional neural network (CNN) is a type of feed-forward neural network (FNN) in which the connectivity pattern between its neurons is inspired by an animal's visual cortex. In the last few years, CNNs have demonstrated superhuman performance in image search services, self-driving cars, automatic video classification, voice recognition, and natural language processing (NLP).

Considering these motivations, in this chapter, we will construct a simple CNN model for image classification from scratch, followed by some theoretical aspects, such as convolutional and pooling operations. Then we will discuss how to tune hyperparameters and optimize the training time of CNNs for improved classification accuracy. Finally, we will build the second CNN model by considering some best practices. In a nutshell, the following topics will be covered in this chapter:

• CNN architectures and drawbacks of DNNs
• The convolution operations and pooling layers
• Creating and training a CNN for image classification
• Model performance optimization
• Creating an improved CNN for optimized performance