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.
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