CNN

Convolutional Neural Networks (CNN) are a class of deep learning models, most commonly applied to analyzing visual imagery. They are also known as ConvNets.

Introduction

Convolutional Neural Networks (CNN) are a type of artificial neural network designed to mimic the connectivity pattern of neurons in the human brain. They are particularly effective for analyzing visual data and are widely used in image recognition and computer vision tasks.

Structure

A typical CNN consists of one or more convolutional layers, followed by pooling layers, fully connected layers, and finally a softmax function for classification. Each layer in a CNN transforms the input data to bring out increasingly complex features.

Convolutional Layer

The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input and producing a 2-dimensional activation map of that filter.

Pooling Layer

The pooling layer (also known as a downsampling layer) is typically applied after the convolutional layer. The function of the pooling layer is to progressively reduce the spatial size of the representation, thereby reducing the amount of parameters and computation in the network.

Fully Connected Layer

Fully connected layers connect every neuron in one layer to every neuron in another layer. It is in principle the same as the traditional multi-layer perceptron neural network (MLP).

Applications

CNNs have been successfully applied to a range of applications, including image recognition, video analysis, and natural language processing. In the field of medicine, CNNs have shown great promise in tasks such as medical imaging analysis, disease prediction, and drug discovery.

See Also

• Artificial Neural Networks
• Deep Learning
• Image Recognition
• Computer Vision
• Medical Imaging

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