Dropout randomly sets neuron inputs/outputs to zero during training
Dropout (neural networks)
Dropout randomly sets neuron inputs/outputs to zero during training
Dropout is a regularization technique used to prevent overfitting in neural networks by randomly disabling neurons during training. This randomness helps the network learn more robust features that are not reliant on specific neurons.
Example
During training, if a neuron has a 50% chance of being dropped out, the input to that neuron will be set to zero for that training instance.
Dropout reduces the risk of overfitting by ensuring that the neural network does not become overly reliant on any single neuron, promoting better generalization.
Related concepts
dropout works as regularization: it approximates an ensemble of subnetworks
Dropout randomly deactivates neurons during training, simulating an ensemble of subnetworks, thus preventing co-adaptation and improving generalization
ill-conditioned matrices cause numerical instability: small input changes → large output changes
Ill-conditioned matrices amplify input perturbations, leading to significant output variability
AdaGrad's learning rate decays to zero
AdaGrad adjusts learning rate by accumulating squared gradients, causing it to decay to zero as denominator grows exponentially
gradient accumulation simulates larger batch sizes without more memory
Gradient accumulation reduces memory usage by dividing a large batch into smaller mini-batches, accumulating gradients before updating model weights
log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily
Log-loss penalizes confident incorrect predictions more heavily
learning rate warmup does: starts small to avoid early training instability
Learning rate warmup gradually increases the learning rate from zero to a predefined value to stabilize training initially
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