In a feedforward or recurrent neural network, a layer of neurons that is neither the input layer nor the output layer but is physically between the two.
In a feedforward or recurrent neural network, a layer of neurons that lies between the input layer and the output layer.
A type of neuron layer that lies between a neural net's input and output layers. Called "hidden", because its neuron values are not visible outside the net. The usage of hidden layers extends a neural net's abilities to learn logical operations.
is the layer of neurons which is not directly connected to the network inputs or outputs.
A layer of neurons in an artificial neural network which does not connect to the outside world but connects to other layers of neurons. All layers of a neural network except the input and output layers, which provide its non-linear modeling capabilities.
A third layer of units between the input and the output layers that provides additional computational power.
A layer of processing elements between a neural network's input layer and its output layer.
In artificial neural networks, a layer of nodes between the input and output layers that contains the weights and processes data. The values within the hidden layer are constantly adjusted during the training of the neural network, until the desired output is reached.
