Significance of Learning and Memories in Computational Methods

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K. Saruladha
Kuraku Nirmala

Abstract

Learning is perpetual, support for learning is memories. Making machine learn requires abundant utilization of memory structures. Neural Networks plays catalytic role for the artificial intelligence and learning processes. Deeper learning was made possible with multiple neural networks playing in synchronization with artificial intelligence, machine learning and convolutional neural networks. Convolution is the basic psychological phenomenon right from the infancy to the adulthood in learning and classifying the real world inputs. Images can be understood by classifying the color palletes and the schemes. Voice is is differentiated with the help of acoustic models and the infra sound detection models. Contribution of Convolution Neural Networks is a great gift to the world in making the machine understands the vitals aspects of sound and images. In this paper a study is presented on the influences of convolutional neural networks and long short-term memory networks that impose new ideas to develop intensive learning frameworks.

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How to Cite
K. Saruladha, & Nirmala, K. (2020). Significance of Learning and Memories in Computational Methods. Helix, 10(02), 219-225. Retrieved from http://helixscientific.pub/index.php/Home/article/view/134
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