dropout layers
Dropout layers are a type of neural network layer that randomly ignores/drops out a certain percentage of input values during training, which helps prevent overfitting and improves generalization in machine learning models.
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Similar Concepts
- attention layers
- autoencoder layers
- batch normalization layers
- boundary layers
- convolutional layers
- dropout regularization
- embedding layers
- fully connected layers
- long short-term memory (lstm) layers
- multilayer perceptrons
- pooling layers
- recurrent layers
- residual layers
- softmax layers
- transformer layers