Demonstration of how convolutional neural networks (CNNs) work for pattern recognition #science

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Convolutional Neural Networks (CNNs) are a class of deep neural networks that are used for image processing and classification. They consist of several layers, each performing a specific operation.

The first layer in a convolutional network is the convolution layer. It performs image convolution with a filter of a certain size. The filter, also called the kernel, traverses the image, calculating the scalar product of the values of the pixels it overlays and the corresponding kernel values. This process creates feature maps, which are then passed to the next layer.

The next layer is the Pooling layer. Its task is to reduce the size of the feature maps, which simplifies further processing. There are several pooling methods, such as max pooling and average pooling, but their general idea is that each part of the feature map is pooled into a single value.

This is followed by several convolution and pooling layers, and then by a Fully Connected layer. In this layer, each neuron is connected to all neurons of the previous layer. The softmax function is used to classify images, which calculates the probabilities of belonging to classes.

The convolutional neural networks efficiently work with images by exploiting the properties of their structure #science #experiment #scienceexperiment