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
More in Deep Learning
Attention Mechanism
A technique that allows models to focus on relevant parts of the input when producing output.
Convolutional Neural Network (CNN)
A neural network architecture designed for processing grid-like data such as images.
Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
LSTM (Long Short-Term Memory)
An RNN variant with gates that control information flow, enabling learning of long-term dependencies.
Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data with connections between nodes forming cycles.
Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
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