Procedia Computer Science · 2018 · 732 citations · 36 references
ComputingComputer ScienceArtificial IntelligenceComputer VisionMachine LearningDeep LearningNeural NetworksPattern RecognitionRepresentation LearningConvolutional Neural NetworksDeep Conceptual UnderstandingDeep Processing
Deep learning has attracted researchers in recent years, with Convolutional Neural Networks widely used to solve complex problems and surpass traditional machine learning limitations. The study aims to provide a conceptual understanding of CNN, covering its three most common architectures and learning algorithms, to serve as a resource and quick reference that broadens researchers' comprehension and motivates further exploration. The authors present a conceptual framework of CNN, detailing its three most common architectures and learning algorithms.
Deep learning has become an area of interest to the researchers in the past few years. Convolutional Neural Network (CNN) is a deep learning approach that is widely used for solving complex problems. It overcomes the limitations of traditional machine learning approaches. The motivation of this study is to provide the knowledge and understanding about various aspects of CNN. This study provides the conceptual understanding of CNN along with its three most common architectures, and learning algorithms. This study will help researchers to have a broad comprehension of CNN and motivate them to venture in this field. This study will be a resource and quick reference for those who are interested in this field.
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