Deep Convolutional Neural Networks

Abstract

This chapter discusses different software programs that are available for deep neural network learning. Training a deep learning model with a limited number of training images is a challenging task due to the number of learnable parameters. The chapter also discusses convolutional neural networks (CNNs) because these models are more applicable to the computer vision field. It aims to serve as a guide for specialists starting in deep learning/computer vision. The chapter provides a comprehensive foundation for deep CNNs. It presents some later developments in various aspects of CNNs, including the convolutional layer, pooling layer, activation function, fully connected layers, regularization, optimization, normalization, and network depth, and we present the advances in each phase. The chapter describes the network design of the proposed system and provides simulation results and evaluation criteria. It focuses on different types of CNNs, which are region-based CNNs, fully convolutional networks, and hybrid learning networks.