Both the input and output layers of neural networks (NN) are included, as well as hidden nodes containing units that convert input into the output so that the output layer may use the value.
This is accomplished using a variety of ideas such as genetic algorithms, fuzzy logic, and the Bayesian gradient-based training approach. Basic object relationships rules are supplied to ANNs.
One of the most significant technological hurdles is the time it takes to train networks, which frequently demand an acceptable level of computational power for even complex tasks.
The most fascinating feature of neural networks is the possibility of developing ‘conscious’ networks in the future.