Extensible neural network class with backpropagation learning, tested on MNIST dataset
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Updated
Nov 23, 2021 - C++
Extensible neural network class with backpropagation learning, tested on MNIST dataset
Résolution numérique parallèle d’un problème stationnaire sur un domaine discret. Il se concentre sur la mise en œuvre de méthodes itératives parallélisées de type Jacobi et Gradient Conjugué pour résoudre des systèmes linéaires dérivés d’une discrétisation par éléments finis.
Collection of practical exercises for the "C++ Programming" in the Master 1 of Applied Mathematics at Sorbonne University
Hybrid-Incomplete-Factorization preconditioners with Iterative Refinements for KSP solvers.
Master's Project on Geometric multigrid preconditioners for the Poisson problem using the deal.II FEM library
discrete optimization problem example with coinMP
Compute lots of Riemann Zeta function values with arbitrary precision
Discretizations of Exterior Calculus for Analysis, Geometry and Topology
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