Artificial Neural Network: Its Features and Examples

What exactly are Artificial Neural Networks? ANNs or simulated neural networks (SNNs) are a subgroup of machine learning that provides the base of deep learning skills. The human brain inspires their name and form, and they replicate the way actual neurons connect with one another.


Artificial neural networks (ANNs) are made up of node layers, each of which has an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, is linked to another and has its own weight and threshold. If the output of any particular node exceeds the given threshold value, that node is activated and begins transferring data to the network's next tier.


Characteristics of ANN


It is a mathematical model that has been neurally implemented.



• To perform all functions, it comprises many linked processing components known as neurons.
• The information held in neurons is essentially the weighted connection of neurons.
• The input signals are routed through connections and linking weights to the processing component
• It can learn, recall, and generalize from given data by assigning and adjusting weights appropriately.
• Neurons' aggregate activity characterizes their processing capability, and no single neuron contains unique information.

Artificial neural networks are designed to simulate the human brain digitally. The Artificial Neural Network used for complicated studies in a variety of sectors ranging from medical to engineering, and these networks have the potential to be utilized in the construction of the next generation of computers. Artificial neural networks have already become an important element of the game business.


Examples of Neural Network Models


The types of Artificial Neural Networks are as follows:



• Perceptron
• Feed Forward Neural Network
• Convolutional Neural Network
• Multilayer Perceptron
• Radial Basis Functional Neural Network
• LSTM – Long Short-Term Memory
• Recurrent Neural Network
• Sequence to Sequence Models
• Modular Neural Network

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