Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.
Keywords
Deep Machine Learning (DML), Federated Learning (FL), Machine Learning (ML).
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Ivan Leonid
Ivan Leonid
Faculty of Computer Science, HSE University, Moscow, Russia.
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Cite this article
Ivan Leonid, “Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms", vol.2, no.4, pp. 132-142, October 2022. doi: 10.53759/181X/JCNS202202016.
