Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
An artificial neural network is the computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
CNN: Convolution preserves the relationship between pixels by learning image features using small squares of input data. It is a mathematical operation that takes two inputs such as image matrix and a filter or kernel. — Dr. Robert Hecht-Nielson
A convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks.
ANN(Artificial neural network) : it is used for data related work and prediction. ex: credit card fraud detection
CNN (Convolutional neural network) : Its used when we have to process the image/text. example face recognition
The major difference between a traditional Artificial Neural Network (ANN) and CNN is that only the last layer of a CNN is fully connected whereas in ANN, each neuron is connected to every other neurons. It is suitable for spatial data such as images.