Dropout
A regularization technique that randomly drops neurons during training to prevent overfitting.
In-depth explanation
During each training step, dropout randomly sets a fraction of neuron outputs to zero. This prevents neurons from co-adapting too much and forces the network to learn more robust features. At inference time, all neurons are used but outputs are scaled. Dropout rate typically ranges from 0.1 to 0.5.
Examples
Related terms
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A neural network architecture designed for sequential data with connections between nodes forming cycles.
LSTM (Long Short-Term Memory)
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Transformer
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Attention Mechanism
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Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
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