This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

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

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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
AI timeline
[ Normal View | Magnified View ]
Components for weather forecasting
[ Normal View | Magnified View ]
Understanding the inter domain relationships
[ Normal View | Magnified View ]
Combining convolutional neural networks and long short‐term memory networks for image description
[ Normal View | Magnified View ]
A long short‐term memory network and memory cell
[ Normal View | Magnified View ]
The LeNet convolutional neural network architecture
[ Normal View | Magnified View ]
The process of backpropagation
[ Normal View | Magnified View ]
High‐level rules‐based system
[ Normal View | Magnified View ]
A sample decision tree
[ Normal View | Magnified View ]
Output from a clustering classifier
[ Normal View | Magnified View ]
The basic perceptron
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Application Areas > Business and Industry
Application Areas > Industry Specific Applications

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts