Conjugate gradient method

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Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

TR global convergence (end) + CG

Covers the trust-region method and introduces the truncated conjugate gradients method.

Iterative Methods for Linear Equations

Introduces iterative methods for linear equations, convergence criteria, gradient of quadratic forms, and classical force fields in complex atomistic systems.

Optimization Basics

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Gradient Descent with Momentum

Explores the use of momentum in gradient descent to enhance speed and stability.

Regression & Systemed Lineaires

Covers the principles of regression and linear systems, focusing on iterative methods.

Truncated CG: Conjugate Gradients

Covers the truncated conjugate gradients algorithm for solving positive definite linear maps iteratively.

Mathematics of Data: Computation Role

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Convergence Criteria: Necessary Conditions

Explains necessary conditions for convergence in optimization problems.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.