Deep Learning Models and Applications: A Review
Abstract
This Deep learning is a forthcoming field ofMachine Learning (ML). Deep Learning (DL) consists of several hidden layers identical to artificial neural networks. Its strategies comprises of supervised and unsupervised learning techniques to automatically learn the hierarchical representation of deep architecture. As data is becoming larger, deep learning is benefiting in this scenario to deal with such enormous amount of resources. Deep learning methodology related with non-linear transformation to abstract furthermore features in high level data representation. The recent development of architecture and techniques in deep learning shows wide application in various fields using artificial intelligence. As deep learning is becoming popular, it is of utmost importance that users should be efficiently equipped with how the model works, how it fails, how the performance is improved, etc. This paper focuses on describing the notion of deep learning along with its progression. The paper also presents some of the most popular DL models and various application areas where it can be applied. This survey can help researchers to have a proper understanding of the widely used DL models and their applications.
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