Cubic Hermite spline

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Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Explores B-spline properties, gradient operators, interpolation, and differentiation filters in image processing.

Covers the basics of parametric curves, including straight lines, quadratic curves, Bézier curves, and cubic curves.

Covers piecewise linear and spline interpolation methods, stability, convergence, and data interpolation using splines.

Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.

Explores cubic splines and least-squares approximation, focusing on interpolation methods and error analysis.

Explores penalised likelihood and natural cubic splines, showcasing the unique explicit solution and the optimality of spline interpolation.

Explores equivalent degrees of freedom in spline smoothing and the bias-variance tradeoff in fitting data.

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Covers advanced numerical analysis topics including deep neural networks and optimization methods.