How convolutional neural network works?

Question

Can anyone explain how the convolutional neural network works?

Answer 1

Convolutional neural network is a deep neural network that takes images as input and extracts the features from input data. Every image is separated by three planes such as red, green, and blue layers and each layer is a matrix of pixel values. Theses layers are processed by using mathematical operations such as convolutions to extract the features and pooling to reduce the computational power needed. The final output is flattened to one layer and sends it to the fully connected layer for classification.

Some of the most used convolutional neural network architectures are LeNet, AlexNet, VGGNet, GoogLeNet, ResNet.

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You can watch this video on How CNN works to understand more about pooling: