A Comprehensive Study of Deep Learning Architectures, Applications and Tools - Ganatra, Nilay; Patel, Atul (International Journal of Computer Sciences and Engineering, 2018)
The Deep learning architectures fall into the widespread family of machine learning algorithms that are based on the model of artificial neural network. Rapid advancements in the technological field during the last decade have provided many new possibilities to collect and maintain large amount of data. Deep Learning is considered as prominent field to process, analyze and generate patterns from such large amount of data that used in various domains including medical diagnosis, precision agriculture, education, market analysis, natural language processing, recommendation systems and several others. Without any human intervention, deep learning models are capable to produce appropriate results, which are equivalent, sometime even more superior even than human. This paper discusses the background of deep learning and its architectures, deep learning applications developed or proposed by various researchers pertaining to different domains and various deep learning tools.