Lecture

Convolutional Neural Networks for Image Recognition

CNN (Convolutional Neural Network) is a type of neural network architecture specialized for analyzing image data.

CNN consists of multiple layers, each playing a role in extracting significant information from images.


Why CNN is Necessary

A traditional Artificial Neural Network (ANN) learns by converting images into a flat array of numbers.

However, this approach can strip away the original shape and structure of the image, potentially losing vital information.

For instance, with a picture of a cat, the positions of the cat's eyes, nose, and ears are important, but converting to simple numbers may fail to maintain these relationships.

CNNs are neural networks capable of learning while maintaining the patterns and structures within an image.

Just as humans recognize faces by considering the positions of eyes, nose, and mouth, CNN finds specific features (like edges, color patterns) within images.

This ability allows a CNN to accurately recognize the same cat in various sizes and positions.


In the next lesson, we'll explore the main components of CNN.

Mission
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다음 중 CNN이 필요한 이유는 무엇인가요?

이미지를 더 빠르게 처리하기 위해

이미지의 모든 픽셀을 정확하게 인식하기 위해

이미지의 패턴과 구조를 유지하면서 학습하기 위해

이미지의 색상을 더 잘 구분하기 위해

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