Cardiovasc Prev Pharmacother.  2021 Jul;3(3):64-72. 10.36011/cpp.2021.3.e9.

Perceptron: Basic Principles of Deep Neural Networks

• Kim EH
• Kim HS

• KMID: 2536942
• DOI: http://doi.org/10.36011/cpp.2021.3.e9

Abstract

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.

Keyword

Artificial intelligence; Deep learning; Machine learning; Neural networks, computer

Figure

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  Figure 1. Training data expressed by vector in 2-dimensional space.

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  Figure 2. Understanding of the meaning of the operation as a physical movement. (A) Two dimensional sample vector v→ and u→, (B) geometric representation of vector addition and (C) geometric representation of vector subtraction.

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  Figure 3. Model that classifies learning data in a 2-dimensional space using straight lines.

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  Figure 4. Perceptron update effect where blue w→: current perceptron, x→: misclassified data by w→, green w→: updated perceptron. (A) representation of update mechanism for w→ that classifies x→ whose label is −1 as +1 and (B) representation of update mechanism for w→ that classifies x→ whose label is +1 as −1.

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