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Eung-Hee Kim 1 Article
Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2021;3(3):64-72.   Published online July 31, 2021
DOI: https://doi.org/10.36011/cpp.2021.3.e9
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Abstract PDF
Big data, artificial intelligence, machine learning, and deep learning have received considerable attention in the medical field. Attempts to use such machine learning in areas where medical decisions are difficult or necessary are continuously being made. To date, there have been many attempts to solve problems associated with the use of machine learning by using deep learning; hence, physicians should also have basic knowledge in this regard. Deep neural networks are one of the most actively studied methods in the field of machine learning. The perceptron is one of these artificial neural network models, and it can be considered as the starting point of artificial neural network models. Perceptrons receive various inputs and produce one output. In a perceptron, various weights (ω) are given to various inputs, and as ω becomes larger, it becomes an important factor. In other words, a perceptron is an algorithm with both input and output. When an input is provided, the output is produced according to a set rule. In this paper, the decision rules of the perceptron and its basic principles are examined. The intent is to provide a wide range of physicians with an understanding of the latest machine-learning methodologies based on deep neural networks.

CPP : Cardiovascular Prevention and Pharmacotherapy