# Gradient descent

Record: term-gradient-descent · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/gradient-descent/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-gradient-descent) · [JSON](https://theaiatlas.org/records/term-gradient-descent.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/100`. Reviewed: 2026-09-15.

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## Publication dates and source age

Publication dates are unavailable.

Newest dated source: unavailable. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

A way to adjust model settings step by step in a direction that aims to reduce training error.

Claim: claim-term-gradient-descent-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[google-gradient-descent](https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descent)

## /definition

Software calculates how a small change to each parameter would affect the loss, then updates the parameters in the opposite direction. Repeating these steps is part of many training procedures.

Claim: claim-term-gradient-descent-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[google-gradient-descent](https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descent)

## /placement

This explains what changing weights during training means in concrete computational terms.

Claim: claim-term-gradient-descent-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[google-gradient-descent](https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descent)

## /distinction

Reducing error on the training objective does not guarantee good results on new examples. Training and evaluation answer different questions.

Claim: claim-term-gradient-descent-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[glossary-model-datasets](https://developers.google.com/machine-learning/crash-course/overfitting/dividing-datasets)

## Source provenance

### google-gradient-descent

[Linear regression: Gradient descent](https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descent)

Google for Developers · First-hand source (primary) · Published: undated · Updated: 2026-02-03 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read the iterative prediction, loss and parameter-update explanation. The page's guarantees for convex linear regression are not extended here to neural-network training. Updated date follows the page; initial publication is unspecified.

No archive check recorded.

### glossary-model-datasets

[Datasets: Dividing the original dataset](https://developers.google.com/machine-learning/crash-course/overfitting/dividing-datasets)

Google for Developers · First-hand source (primary) · Published: undated · Updated: 2025-12-03 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read training, validation and test separation, duplicate examples and repeated test reuse. Course examples illustrate evaluation problems; no model was tested here.

No archive check recorded.