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(Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning relies on neural networks, which are capable of learning from large datasets to detect patterns and make predictions. Neural networks consist of multiple layers connected nodes where each node performs a simple mathematical operation on its inputs. The output from one layer feeds into the next until an ultimate prediction is produced. Hidden layers play an integral role in these neural networks and pl...")
 
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{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
===Introduction==
==Introduction==
Machine learning relies on neural networks, which are capable of learning from large datasets to detect patterns and make predictions. Neural networks consist of multiple layers connected nodes where each node performs a simple mathematical operation on its inputs. The output from one layer feeds into the next until an ultimate prediction is produced. Hidden layers play an integral role in these neural networks and play an essential role during the learning process.
A [[neural network]] consists of multiple layers: the [[input layer]], where [[data]] is first introduced, and the [[output layer]], where [[prediction]]s are made. Between these layers may exist one or more [[hidden layer]]s which cannot be observed directly by humans and do not form part of either [[input or output [[datasets]]. Instead, hidden layers provide an intermediate representation of input that the neural network can use to make its predictions.


==What is a Hidden Layer?==
Each [[node]] or [[neuron]] in a hidden layer uses the output from its predecessor as input and applies an [[optimization function]] to it. Usually [[non-linear]], this helps the neural network capture complex patterns in data. The output from these nodes is then fed back into the next hidden layer, with this process continuing until an output layer produces a prediction.
A neural network consists of two layers: the input layer, where data is first introduced, and the output layer, where predictions are made. Between these layers may exist one or more hidden layers which cannot be observed directly by humans and do not form part of either input or output data sets. Instead, hidden layers provide an intermediate representation of input that the neural network can use to make its predictions.


Each node in a hidden layer uses the output from its predecessor as input and applies an optimization function to it. Usually non-linear, this helps the neural network capture complex patterns in data. The output from these nodes is then fed back into the next hidden layer, with this process continuing until an output layer produces a prediction.
The number of hidden layers and nodes per layer are key [[hyperparameters]] that can significantly influence the performance of a neural network. Too few hidden layers or nodes may prevent your network from learning complex patterns, while too many could lead to [[overfitting]]--wherein one performs well on [[training data]] but poorly on new data.
 
The number of hidden layers and nodes per layer are key parameters that can significantly influence the performance of a neural network. Too few hidden layers or nodes may prevent your network from learning complex patterns, while too many could lead to overfitting--wherein one performs well on training data but poorly on new data.


==Why are Hidden Layers Important?==
==Why are Hidden Layers Important?==
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