What is a convergence?
A state reached when loss values change very little or not at all with each iteration.
convergence explained in plain English
A state reached when loss values change very little or not at all with each iteration. For example, the following loss curve suggests convergence at around 700 iterations: A model converges when additional training won't improve the model. In deep learning, loss values sometimes stay constant or nearly so for many iterations before finally descending. During a long period of constant loss values, you may temporarily get a false sense of convergence. See also early stopping. See Model convergence and loss curves in Machine Learning Crash Course for more information.
Example
Practitioners refer to convergence 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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The set of examples used in one training iteration.
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The number of examples in a batch.
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A neural network containing more than one hidden layer.
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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.
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A form of regularization useful in training neural networks.