Deep Learning

Abstract

This chapter provides a comprehensive explanation of deep learning including an introduction to ANNs, improving the deep NNs, CNNs, classic networks, and some technical tricks for image classification using deep learning. ANNs, mathematical models for one node ANN, and multi-layers/multi-nodes ANNs are explained followed by the ANNs training algorithm followed by the loss function, the cost function, the activation function with its derivatives, and the back-propagation algorithm. This chapter also outlines the most common training problems with the most common solutions and ANNs improvements. CNNs are explained in this chapter with the convolution filters, pooling filters, stride, padding, and the CNNs mathematical models. This chapter explains the four most commonly used classic networks and ends with some technical tricks that can be used in CNNs model training.