An Overview of Deep Learning Architecture of Deep Neural Networks and Autoencoders

Abstract

The recent wide applications of deep learning in multiple fields has shown a great progress, but to perform optimally, it requires the adjustment of various architectural features and hyper-parameters. Moreover, deep learning could be used with multiple varieties of architecture aimed at different objectives, e.g., autoencoders are popular for un-supervised learning applications for reducing the dimensionality of the dataset. Similarly, deep neural networks are popular for supervised learning applications viz., classification, regression, etc. Besides the type of deep learning architecture, some other decision criteria and parameter selection decisions are required for determining each layer size, number of layers, activation and loss functions for different layers, optimizer algorithm, regularization, etc. Thus, this paper aims to cover different choices available under each of these major and minor decision criteria for devising a neural network and to train it optimally for achieving the objectives effectively, e.g., malware detection, natural language processing, image recognition, etc.