Gradient descent
Publication dates and source age
Sources counted: 2
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
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In plain language
A way to adjust model settings step by step in a direction that aims to reduce training error. [1]
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Limits & distinctions
Reducing error on the training objective does not guarantee good results on new examples. Training and evaluation answer different questions. [2]
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A fuller explanation
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. [1]
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How it relates to the map
This explains what changing weights during training means in concrete computational terms. [1]
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https://theaiatlas.org/ideas/gradient-descent/
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Sources and what we read
1. Linear regression: Gradient descent
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
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.
2. Datasets: Dividing the original dataset
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read training, validation and test separation, duplicate examples and repeated test reuse. Course examples illustrate evaluation problems; no model was tested here.
Edition and machine-readable evidence
Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.
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