Analysis and presenting the educational techniques in Machine and Deep Learning Short communication
DOI:
https://doi.org/10.14419/ijet.v7i4.6.20716Published:
2018-09-25Keywords:
Artificial Intelligence, Cubic centimeter, Deep learning, Machine Learning, Metric Capacity.Abstract
This paper gives a present of general learning of deep methodology and its applications to a variety of signal and data processing schedules. It is discussed about Machine learning vs. Deep Learning a brief and which is best suited in the market, Dissimilarities, Problem handling, Interpretability, Comparative and different options between cubic centimeter and metric capacity unit and concluded by justifying deep learning is a part of Machine learning and Machine learning is a part of Artificial intelligence.
References
[1] “Machine Learning for Image Reconstruction†Ge Wang, Jong Chu Ye, Klaus Mueller, Jeffrey A. Fessler May 2, 2018.
[2] Temporal Dynamics of Learning for Resolution of super video: A Learning of deep Approach Ding Liu, Student Member, IEEE, Zhaowen Wang, Member, IEEE, Yuchen Fan, Xianming Liu, Zhangyang Wang, Member, IEEE, Shiyu Chang, Xinchao Wang, and Thomas S. Huang, Life Fellow, IEEE.
[3] Intelligent tuninf of parameter in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning Chenyang Shen, Yesenia Gonzalez, Liyuan Chen, Steve B. Jiang, Xun Jia.
[4] Deep Learning: Applications and Methods Li Deng, Microsoft Research. One Microsoft Way Redmond, WA 98052, USA deng@microsoft.com, Dong Yu, Microsoft Research, One Microsoft way Redmond, WA 98052, USA, Dong.Yu@microsoft.com
[5] Deep Monocular Depth Estimation via Integration of Global and Local Predictions Youngjung Kim, Student Member, IEEE, Hyungjoo Jung, Student Member, IEEE, Dongbo Min, Senior Member, IEEE, and Kwanghoon Sohn, Senior Member, IEEE.
[6] Image Reconstruction Is a New Frontier of Machine Learning— Editorial for the Special Issue
How to Cite
License
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 Licensethat allows others to share the work with an acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).
Accepted 2018-10-01
Published 2018-09-25