The interactive film is in the book's Companion

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Hessian Matrix: Bowls, Saddles and Curvature Directions

Chapter 11 · Mathematics for AI and Machine Learning · Advanced · 1:44

An interactive live film: the Hessian eigenvalues govern curvature — watch two positive numbers form a bowl, while opposite signs flip the local surface into a saddle.

Narration

Here is a landscape with both a valley and a pass: f equals x zero cubed minus three times x zero plus x one squared, and a point a on the floor.

Start at one and zero, on the right. Both principal curvatures are positive: six along the red curve, and two along the green curve. The local quadratic approximation, the blue surface, is a bowl curving upward in every direction.

The rest of the narration is in the book's Companion.

More from Chapter 11

Interactive1:54Backpropagation, One VJP per LayerChapter 11Preview1:27Partial Derivatives Are Two SlicesChapter 11Preview1:34The Gradient Is the Steepest DirectionChapter 11Preview1:42Jacobian Matrix: Linearizing a MapChapter 11Preview1:43The Derivative of the Matrix InverseChapter 11Preview1:54Forward or Reverse: Filling the JacobianChapter 11Preview1:32Taylor Expansion, One Term at a TimeChapter 11Preview1:30Second-Order Taylor: Where the Quadratic Model Can Be TrustedChapter 11Preview1:38A Hessian-Vector Product from Two GradientsChapter 11Preview1:34The Gauss–Newton MatrixChapter 11Preview1:37Why the Hessian Is SymmetricChapter 11Preview1:26The Newton Step: Jumping to the Taylor Bowl's BottomChapter 11Preview2:36Corners, Maxima and Steps in the Chain RuleChapter 11Preview1:59GPTQ: Rounding with the HessianChapter 11Preview2:12AWQ: Activation-Aware Weight QuantizationChapter 11Preview1:41The Implicit Function TheoremChapter 11