Harnessing Deep Learning: Innovations and Practical Applications

In the recent years, Deep learning is an emerging and exciting subfield in machine learning. The purpose of deep learning technique is to deal with complex problems with huge amounts of data. In this paper, the author focuses on the vast concepts of deep learning. Firstly, discussion for the architecture of deep learning, which is classified into six categories like auto encoder, convolution neural network, restricted Boltzmann machine, recurrent neural network, long short-term memory, granted recurrent unit network and deep stacking network. Secondly, it offers the various applications and the discussion about the recent trends of deep learning in several fields i.e. big data, healthcare, object detection, plant classification, natural language processing, cybersecurity, parking system, speech recognition. Thirdly, the main intention is to discuss the advantages, disadvantages, and challenges of deep learning techniques for analysis. Finally, the paper ends with the conclusion and potential avenues for future in the field of research.