Our brain processes things in a pictorial fashion. It tries to look for features and identify or classify objects in our surroundings. Since, our aim with neural networks is to mimic the human brain, a convolutional neural network (CNN) is mechanised such that it looks for features in an object.
CNN has the following five basic components:
- Convolution : to detect features in an image
- ReLU : to make the image smooth and make boundaries distinct
- Pooling : to help fix distored images
- Flattening : to turn the image into a suitable representation
- Full connection : to process the data in a neural network
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