What is a hidden layer?
A layer in a neural network between the input layer (the features) and the output layer (the prediction).
hidden layer explained in plain English
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: 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.
Example
Practitioners refer to hidden layer when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- batch
The set of examples used in one training iteration.
- batch normalization
Normalizing the input or output of the activation functions in a hidden layer.
- batch size
The number of examples in a batch.
- Bayesian neural network
A probabilistic neural network that accounts for uncertainty in weights and outputs.
- co-adaptation
An undesirable behavior in which neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network's behavior as a whole.
- convergence
A state reached when loss values change very little or not at all with each iteration.
- deep model
A neural network containing more than one hidden layer.
- depth
The sum of the following in a neural network: - the number of hidden layers - the number of output layers, which is typically 1 - the number of any embedding layers For example, a neural network with five hidden layers and one output layer has a depth of 6.