ANN is a neural network which tries to perform tasks like a human does, think like a human brain. Just like a human brain understands things after learning by watching things or by experience, ANN does the same as well. It learns with experience of going through the dataset multiple times and understands the relations, hidden features and parameters. ANN is helpful in doing regression, classification tasks and performs extremely well on huge datasets achieving high accuracy.
Input Layer:
Whatever input you pass for the model to learn goes through this layer of neural network for performing calculations.
Hidden Layer:
The layer as the name suggests hidden because when we see the real time application we only focus on the input and output, we do not focus on how things happen. Hidden layer performs calculations, does processing, understands the hidden features and updates weights to get the best possible accuracy.
Output Layer:
The input passes through hidden layer where processing happens and output is returned.
For detailed explanation of ANN, refer to this blog https://ai.plainenglish.io/artificial-neural-network-ann-edec89ce4daa .
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