What is an iteration?
A single update of a model's parameters—the model's weights and biases—during training.
iteration explained in plain English
A single update of a model's parameters—the model's weights and biases—during training. The batch size determines how many examples the model processes in a single iteration. For instance, if the batch size is 20, then the model processes 20 examples before adjusting the parameters. When training a neural network, a single iteration involves the following two passes: 1. A forward pass to evaluate loss on a single batch. 2. A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate. See Gradient descent in Machine Learning Crash Course for more information.
Example
Practitioners refer to iteration 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.