Heterogenous Applications of Deep Learning Techniques in Diverse Domains: A Review

Abstract

Deep learning (DL) techniques have recently emerged as the most significant techniques for processing big multimedia data. DL networks autonomously extract advanced and inherent features from the big data sets using systematic learning methods. The real-world problem-solving using DL techniques demands large parallel computing infrastructure facilities for achieving high efficiency. Recent developments in deep learning techniques have demonstrated that it could outperform humans in some tasks such as classifying and tracking multimedia data. The deep learning networks can have about 150 hidden layers. The increase in the output performance of deep learning networks is directly proportional to input sample data size. This paper reviewed the literature on applications on deep learning from diverse application domains. Authors have also carried out a comparative study of various DL methods used and highlighted their results.KeywordsDeep learning (DL)Machine learning (ML)Convolution neural networks (CNN)Deep belief networks (DBF)Extreme learning machine (ELM)Deep neural networks (DNN)