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Gradient Descent

Definition
AI-generated

Gradient descent updates model parameters by stepping opposite the gradient of a loss function with respect to weights; stochastic variants estimate gradients on minibatches to scale to large datasets.

Topics
LLM and Agents Machine learning concepts
Synonyms
Steepest descent (batch case, informal)
Related
Backpropagation Batch Deep Learning

Why it matters in GWAS

Deep sequence or single-cell predictors used alongside GWAS pipelines depend on optimization stability; learning-rate schedules, batching, and regularization interact with whether models generalize across ancestries or labs.

Example usage

"The modeling pipeline uses Gradient Descent to improve prediction and feature interpretation before validation."

Backpropagation Batch Deep Learning Genome-Wide Association Study (GWAS) Learning Rate Loss Function Neural Network Optimizer Regularization
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Rumelhart DE, Hinton GE, Williams RJ. (1986). Learning representations by back-propagating errors. Nature.
  • Goodfellow I, Bengio Y, Courville A. (2016). Deep Learning. MIT Press.
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