Analysis and presenting the educational techniques in Machine and Deep Learning Short communication


  • S Rahul
  • . .





Artificial Intelligence, Cubic centimeter, Deep learning, Machine Learning, Metric Capacity.


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.




[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, Dong Yu, Microsoft Research, One Microsoft way Redmond, WA 98052, USA,

[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

View Full Article:

How to Cite

Rahul, S., & ., . (2018). Analysis and presenting the educational techniques in Machine and Deep Learning Short communication. International Journal of Engineering & Technology, 7(4.6), 296–298.
Received 2018-10-01
Accepted 2018-10-01
Published 2018-09-25