Neural networks & deep learning
Publication dates and source age
Sources counted: 5
Newest dated source: 2025-03-27
At least one source was published within the 18-month window.
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.
Open in the glossary Reading notes · Structured record
In plain language
Models built from connected layers of calculations whose settings are learned during training. [1] [2]
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Limits & distinctions
Words such as 'neuron' and 'learning' describe mathematical components and training here. They do not, by themselves, explain a model's behavior in human terms. [1] [5]
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A fuller explanation
Each layer takes numbers from the previous layer, combines them using weights and passes results onward. Deep learning uses networks with multiple internal layers. 'Deep' describes that structure. [1] [3]
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How it relates to the map
This is the underlying model family used by LLMs. [4]
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https://theaiatlas.org/ideas/neural-network/
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Image previews are summaries. Keep the page link so readers can check the evidence.
Sources and what we read
1. Neural networks: Nodes and hidden layers
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 explanations of connected layers, numerical weights and biases, and calculations. Did not run the embedded exercises. Used for the mathematical structure, not a claim that an artificial network reproduces a human brain.
2. 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.
3. Machine Learning Glossary
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 artificial intelligence, deep model, context window, inference and chat entries. Used for terminology, not product performance claims. Updated date follows the earlier displayed page date; initial publication is unspecified. The chat entry was reread during the same-day beginner-content review. Also read the generalization and compute entries.
4. LLMs: What's a large language model?
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 token prediction, encoder-only and decoder-only variants, and self-attention. Used for architecture and terminology; broad performance comparisons and claims about all LLMs on the teaching page are not adopted.
5. Circuit Tracing: Revealing Computational Graphs in Language Models
Publication dates and source age
Sources counted: 1
Newest dated source: 2025-03-27
At least one source was published within the 18-month window.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
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 introduction, method overview and limitations including reconstruction errors, graph complexity, global circuits and mechanistic faithfulness. The authors' replacement-model analyses reveal selected mechanisms; they do not provide a complete explanation of all behavior. Later attention-tracing work is cited alongside this paper to avoid treating its missing-attention limitation as a permanent field-wide result.
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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