What is a deep model?
A neural network containing more than one hidden layer.
deep model explained in plain English
A neural network containing more than one hidden layer. A deep model is also called a deep neural network. Contrast with wide model.
Example
Practitioners refer to deep model 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.
- 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.
- dropout regularization
A form of regularization useful in training neural networks.