Convolution Neural Network

Abstract

Convolutional neural network (CNN) carries spatial information—not all nodes in one layer are fully connected to nodes in the next layer; weights are shared. The main goal of CNN is to process large image pixel matrix and try to reduce high matrix dimensions without losing information, and to simplify the network architecture with weight sharing, reducing the number of trainable parameters in the network, which helped the model to avoid overfitting and as well as to improved generalization and still to give high performance with desired accuracy. So, CNN has become dominant in various computer vision tasks and is attracting interest across a variety of domains involving image processing. This chapter focuses on the foundation of CNN, followed by architecture of CNN, activation functions, applications, and recent trends in CNN.