What is the difference between artificial and convolutional neural networks?

Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.

The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.

Artificial neural networks are (obviously) all non-natural systems mimicking, in one or in several ways, the processing system which is the biological brain of living beings.

Artificial Neural networks (ANN) or neural networks are computational algorithms. … ANNs are computational models inspired by an animal’s central nervous systems. It is capable of machine learning as well as pattern recognition. These presented as systems of interconnected “neurons” which can compute values from inputs

That is, a convolutional NN is a specialized ANN, and so it is also classified as “artificial”.

Examples of CNN (convolutional neural network) in computer vision are:

face recognition, image classification, etc. It is similar to the basic neural network. CNN(convolutional neural network) also has a learnable parameter like neural network i.e, weights, biases, etc.

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