A hidden layer is a layer of neurons in a neural network positioned between the input layer and the output layer. Hidden layers transform input representations into intermediate features that support prediction, classification, or generation. Networks with multiple hidden layers are commonly described as deep neural networks.
A layer of neurons in an artificial neural network that is neither an input layer nor an output layer.
A layer in a neural network between the input layer (the features) and the output layer (the prediction). Each hidden layer consists of one or more neurons. For example, the following neural network contains two hidden layers, the first with three neurons and the second with two neurons: !Four layers. The first layer is an input layer containing two features. The second layer is a hidden layer containing three neurons. The third layer is a hidden layer containing two neurons. The fourth layer is an output layer. Each feature contains three edges, each of which points to a different neuron in the second layer. Each of the neurons in the second layer contains two edges, each of which points to a different neuron in the third layer. Each of the neurons in the third layer contain one edge, each pointing to the output layer. A deep neural network contains more than one hidden layer. For example, the preceding illustration is a deep neural network because the model contains two hidden layers. See Neural networks: Nodes and hidden layers in Machine Learning Crash Course for more information.
A layer in a neural network between the input layer (the features) and the output layer (the inference). Each hidden layer consists of one or more neurons. A deep neural network contains more than one hidden layer.