Thinking·OS

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Explore the knowledge graph

Training data enables Machine learning. Activate to inspect this relation.Machine learning enables Generalization. Activate to inspect this relation.Generalization enables Prediction. Activate to inspect this relation.Machine learning depends on Optimization. Activate to inspect this relation.Supervised learning is part of Machine learning. Activate to inspect this relation.Gradient descent applies to Optimization. Activate to inspect this relation.Gradient descent depends on Training data. Activate to inspect this relation.Supervised learning depends on Training data. Activate to inspect this relation.Supervised learning enables Prediction. Activate to inspect this relation.Supervised learning enables Generalization. Activate to inspect this relation.Optimization landscape applies to Gradient descent. Activate to inspect this relation.Regression is a Supervised learning. Activate to inspect this relation.Classification is a Supervised learning. Activate to inspect this relation.Feature engineering enables Supervised learning. Activate to inspect this relation.Gradient descent requires Loss function. Activate to inspect this relation.Supervised learning depends on Loss function. Activate to inspect this relation.Model evaluation depends on Loss function. Activate to inspect this relation.Regression depends on Gradient descent. Activate to inspect this relation.Regularization is part of Loss function. Activate to inspect this relation.Loss functionGradient descentSupervised learningModel evaluationRegularizationOptimizationTraining dataOptimization landscapeRegressionMachine learningPredictionGeneralizationClassificationFeature engineering
Relationship types
Legend
  • Focused concept
  • Connected concept
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
14 concepts19 relationships14 disciplines4 relation families

Loss function

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At a glance

A loss function quantifies the discrepancy between a model's predictions and the true values.

Disciplines
Data Science
Role in the graph
Cross-disciplinary bridge — reaches Computer Science, Machine Learning, Optimization
Relationships
4 · 2 relation families

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 14 disciplines: Artificial Intelligence, Business, Cognitive Science, Computer Science, Data Science, Economics, Engineering, Machine Learning, Mathematical Modelling, Mathematics, Optimization, Philosophy of Science, Statistics, Systems Engineering.
  • Generalization is a bridge concept — viewed here through Cognitive Science, Machine Learning, Statistics.
  • The connections here span 4 relation families.
  • Optimization explains 2 concepts in this view (Business, Computer Science, Data Science, Economics, Engineering, Machine Learning, Mathematical Modelling, Optimization, Systems Engineering).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.