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Chain Rule — Topic Summaries

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What's so special about Euler's number e? | Chapter 5, Essence of calculus

3Blue1Brown · 2 min read

Exponentials are special in calculus because their derivatives are proportional to the functions themselves—and the constant of proportionality is...

Derivatives of ExponentialsNatural LogarithmBase e

Backpropagation calculus | Deep Learning Chapter 4

3Blue1Brown · 2 min read

Backpropagation’s calculus boils down to one practical question: how much does the cost change when a single weight or bias nudges a network’s...

Backpropagation CalculusChain RuleGradient Descent

e^(iπ) in 3.14 minutes, using dynamics | DE5

3Blue1Brown · 2 min read

The core insight is that the exponential function is uniquely characterized by the rule “rate of change equals the current value,” and swapping the...

Exponential FunctionsDifferential EquationsComplex Numbers

Implicit differentiation, what's going on here? | Chapter 6, Essence of calculus

3Blue1Brown · 3 min read

A calculus “weirdness” becomes manageable once tiny changes in two variables are given a geometric meaning: implicit differentiation is really about...

Implicit DifferentiationTangent SlopesRelated Rates

Visualizing the chain rule and product rule | Chapter 4, Essence of calculus

3Blue1Brown · 2 min read

Derivatives of complicated expressions don’t come from memorizing formulas—they come from tracking how tiny input “nudges” propagate through three...

Sum RuleProduct RuleChain Rule

The Most Important Algorithm in Machine Learning

Artem Kirsanov · 3 min read

Backpropagation is the shared engine behind modern machine learning: it turns the goal of minimizing prediction error into a practical, efficient...

BackpropagationGradient DescentLoss Functions

Backpropagation in CNN | Part 1 | Deep Learning

CampusX · 3 min read

Backpropagation for a simple CNN is built from a clear chain of derivatives: start with the loss from the final prediction, then push gradients...

BackpropagationCNN TrainingBinary Cross-Entropy

Multivariable Calculus 10 | Directional Derivative

The Bright Side of Mathematics · 2 min read

Directional derivatives extend partial derivatives by measuring how a multivariable function changes when moving in an arbitrary direction, not just...

Directional DerivativePartial DerivativesGradient

Multivariable Calculus 7 | Chain, Sum and Factor rule

The Bright Side of Mathematics · 2 min read

Multivariable calculus keeps the same “algebra of derivatives” from one-variable calculus: total differentiation behaves linearly. If two totally...

Total DifferentiabilitySum RuleFactor Rule

Multivariable Calculus 9 | Geometric Picture for the Gradient

The Bright Side of Mathematics · 2 min read

The gradient’s geometric meaning becomes concrete: whenever a curve stays on a level set (a contour line) of a multivariable function, the gradient...

Gradient GeometryContour LinesChain Rule

Multivariable Calculus 21 | Diffeomorphisms

The Bright Side of Mathematics · 2 min read

Diffeomorphisms formalize when a change of coordinates is smooth in both directions—meaning a map has a smooth inverse, not just a smooth forward...

DiffeomorphismsC^k SmoothnessInverse Functions

Manifolds 24 | Differential in Local Charts [dark version]

The Bright Side of Mathematics · 2 min read

The differential on a manifold can be computed in local coordinate charts using the same machinery as multivariable calculus: Jacobian matrices and...

Manifold DifferentialsLocal ChartsTangent Vectors