Your session has been flagged for unusual activity.
Please confirm you're human to continue.
A regularization technique that randomly disables neurons during training to prevent overfitting and improve generalization.
Srivastava et al. (2014) introduced dropout: during each training step, a random fraction of units is temporarily removed, forcing the network not to rely on any single neuron. This acts like training a large ensemble of thinned networks and markedly reduces overfitting.
Dropout became a standard ingredient in deep networks and is still used, in various forms, in modern architectures.
Primary, peer-reviewed and archival sources for this definition.
Run the same prompt across ChatGPT, Claude, Gemini, Grok and Perplexity side by side — with your own files as context and answers you can cross-check.
Try MultipleChat FreeContinue learning
Use several frontier models together for stronger answers and fewer blind spots.
ComparisonCompare several AI answers side by side when the output matters.
PricingSee the AI subscription that replaces separate ChatGPT, Claude, Gemini, Grok and Perplexity accounts.
Multi-model AIRun the same prompt through available ChatGPT, Claude, Gemini and Grok models and compare their answers.
GuideChoose the best model for writing, research, coding, documents, images and business work.
AlternativeUse ChatGPT alternatives together: Claude, Gemini, Grok, Perplexity, files, web search and creation tools.
