Cloud computing for deep learning analytics:A survey of current trends and challenges
Article Sidebar
Published:
Apr 30, 2017
Main Article Content
Abstract
Deep learning, a sub-field of machine learning is inspired by the principle of information processing in the human brain. Its applications are autonomous driving, robotics control, machine translation etc. It needs multiple training examples of the task, specialized GPU hardware, capital investment and its libraries evolve quickly, so frequent updates are needed. Cloud computing is a type of computing in which computing resources are provided on demand. Cloud is an apt choice for a platform for deep learning analytics as it provides servers, storage and networking resources. It provides scalability, processing, storage and analytics resources. Deep learning algorithms like CNN are computationally intensive for a commercial computer for large larger datasets. Cloud computing allows prevailing the processing, memory constraints of average computers, allowing computations on larger datasets. This paper discusses how cloud computing allows us to overcome the constraints of deep learning analytics on average systems and the various platforms offered by various providers. Google, NVIDIA, IBM provide us platforms for deep learning. IBM’s Rescale, Nervana Cloud , Google deep learning cloud provide full-stack hosted platform for deep learning.
Keywords: Deep learning, Artificial intelligence, scalability, virtualization
Keywords: Deep learning, Artificial intelligence, scalability, virtualization
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.