An overview on the evolution and adoption of deep learning applications used in the industry

With continuous improvements in performance of microprocessors over the years, they now possess capabilities of supercomputers of earlier decade. Further with the continuous increase in the packaging density on the silicon and General Purpose Graphics Processing Unit (GPGPU) enhancements, has led to utilization the deep learning (DL) techniques, which had lost steam during the last decade. A GPGPU is a parallel programming setup using a combination of GPUs and CPUs that can manipulate large matrices. Interestingly, GPUs were created for faster graphic processing, but found its way into relevant scientific computing. DL is a subset of the artificial intelligence (AI) domain and falls specifically under the set of machine learning (ML) techniques which are based on learning data representations rather than task‐specific algorithms. It has been observed that the accuracy and the pragmatism of deploying DL at massive level was restricted by technological issues of executing DL based AI models, with extremely large training sessions running into weeks. DL applications can solve problems of very large order and areas like computer vision/image processing is one of the early successes and becoming quite a sensation in many areas such as natural language processing (NLP) with state of the art real‐time translation capabilities, automatic game playing, optical character recognition especially handwritten text, and so on. This overview traverses the evolution and successful adoption in the various industry verticals. This article is categorized under: Application Areas > Industry Specific Applications Application Areas > Business and Industry Technologies > Machine Learning