In the context of artificial neural networks, a layer (hidden layer) is a set of interconnected neurons located between the input layer and the output layer. It's typically called "hidden" because it doesn't directly interact with either the inputs or the output and isn't exposed to the data directly.
How Hidden Layer works
In a neural network, data is input into the input layer and output comes out from the output layer. The hidden layers in between do the essential computational work.
Each neuron in a hidden layer takes in weights, which are adjusted during training, and applies an activation function to calculate an output. This output is then passed onto the next layer. When multiple hidden layers exist, they process the information from the previous layer and pass it to the next layer, enabling the network to learn complex patterns.
The concept behind many hidden layers is that each layer extracts and processes certain features from the input and passes the result further down the network. In a deep learning scenario, layers closer to the input might detect simple features like lines or edges, while layers closer to the output might identify complex combinations of those initial features, like shapes or objects.
The number of hidden layers and the number of neurons in each hidden layer are design choices that can greatly affect the learning capacity and performance of the network. Too few may result in underfitting (the model doesn’t learn enough), and too many may lead to overfitting (the model learns too much from the training data and performs poorly on new, unseen data). Typically, these parameters are decided via trial and error or by using auto-tuning techniques.
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