Artificial Neural Networks (ANN) are statistical models based on the behaviour of human brain cells known as neurons. ANN can mathematically simulate the biological brain, allowing machines to think and learn in the same way that people do, allowing them to recognize speech, objects, and animals in the same manner that humans do. The following are some of the most widely utilized ANNs:
Feedforward Neural Network (FNN)
FNN is the initial and most basic type of ANN, in which the nodes’ connections do not form a cycle. That is, the data or input flows in one direction, passing via the input nodes before exiting on the output nodes. There may or may not be hidden layers in this network.
Convolutional Neural Network (CNN)
CNNs are a form of feed-forward artificial neural network made up of multilayer perceptrons that require little preprocessing. A convolutional neural network, similar to a filter, receives the input data in batches and assigns importance (learnable weights and biases) to various elements or objects in the picture, allowing it to distinguish one from the other. The network can recall pictures in chunks and perform actions on them. It’s primarily utilized for signal and image processing, as well as visual imagery analysis.
Recurrent Neural Network (RNN)
The recurrent neural network operates on the idea of storing a layer’s output and feeding it back into the input to assist anticipate the layer’s outcome. This network processes variable-length sequences of inputs using its internal state (or memory), allowing it to accommodate arbitrary input or output lengths.
Unsupervised data codings are learned by this sort of artificial neural network. Input, output, and hidden layers connect these unsupervised learning models. These are mostly used for dimensionality reduction and building generative models of data by training the network to disregard signal “noise.” It may be used for picture reconstruction and colorization.