Trends in Deep Learning Applications
- 126 Downloads
Deep Learning is a new area of Machine Learning which has gained popularity in the recent past. It has surpassed the conventional algorithms in accuracy as the features are learned from the data using a general purpose learning procedure instead of being designed by human engineers . Deep learning is responsible for today’s explosion of AI. Deep networks have demonstrated dramatic improvements in computer vision and machine translation tasks. It has the ability to recognize spoken words nearly as good as humans can. It has demonstrated good generalization power and has achieved high accuracy in machine learning modeling, which has even attracted non-computer scientists. It is now being used as a guide to make key decisions in fields like medicine, finance, manufacturing, and beyond. Deep learning has succeeded in previously unsolved problems which were quite difficult to resolve using machine learning as well as other shallow networks. However, deep learning is still in its infancy, but it is likely that deep learning will have many successes in the near future as it requires little hand engineering and thus can take advantage of the vast amount of data and computation power. Various supervised and unsupervised deep architectures have been reported in . This chapter outlines the use of deep learning technology in applications like game playing, medical applications, video analytics, regression/classification, object detection/recognition, and robotic automation.
- 1.M. Arif Wani, F.A. Bhat, S. Afzal, A. Khan, Advances in Deep Learning, vol. 57 (Springer, Berlin, 2020)Google Scholar