Pinned Repositories
arc
Numerical experiments for Riemannian ARC project
ASDHA-experimental-results
cheat_sheets
Репозиторий для шпаргалок
Experimental-Data-obtained-from-ASSAs-and-SSGMs
Experimental_Results_of_GSDA_Algorithm
GSDA_for_Robotic_Arm
LSOptProject
Course project "Combinatorial Optimization: Max-Cut, Min Uncut and Sparsest Cut Problems". Marina Danilova, Nikita Puchkin, Alena Shilova. Skolkovo Institute of Science and Technology
MatrixFactorizationMatlab
matrix factorization using python, exploring various gradient methods
mmta_fall_2019
Лекции и материалы по курсу "Математические методы анализа текстов" осеннего семестра 2019 года для студентов кафедры ММП, ВМК МГУ и кафедры ИС, ФУПМ МФТИ
modi
MAHMOUDPD's Repositories
MAHMOUDPD/modi
MAHMOUDPD/arc
Numerical experiments for Riemannian ARC project
MAHMOUDPD/ASDHA-experimental-results
MAHMOUDPD/cheat_sheets
Репозиторий для шпаргалок
MAHMOUDPD/Experimental-Data-obtained-from-ASSAs-and-SSGMs
MAHMOUDPD/Experimental_Results_of_GSDA_Algorithm
MAHMOUDPD/GSDA_for_Robotic_Arm
MAHMOUDPD/LSOptProject
Course project "Combinatorial Optimization: Max-Cut, Min Uncut and Sparsest Cut Problems". Marina Danilova, Nikita Puchkin, Alena Shilova. Skolkovo Institute of Science and Technology
MAHMOUDPD/MatrixFactorizationMatlab
matrix factorization using python, exploring various gradient methods
MAHMOUDPD/mmta_fall_2019
Лекции и материалы по курсу "Математические методы анализа текстов" осеннего семестра 2019 года для студентов кафедры ММП, ВМК МГУ и кафедры ИС, ФУПМ МФТИ
MAHMOUDPD/Scientific-Programming
MAHMOUDPD/SDMSC_project
MAHMOUDPD/Struc.-diag.-Hessian-Algorithms-ASDHAs-for-NLS-Problems
MAHMOUDPD/optim
💎A site, that contains systematic optimization methods and theory review
MAHMOUDPD/Optimization-algorithms
MAHMOUDPD/OptML_course
EPFL Course - Optimization for Machine Learning - CS-439
MAHMOUDPD/random_reshuffling
RR, SO, IG and SGD with logistic regression loss.
MAHMOUDPD/RSOpt
Riemannian stochastic optimization algorithms: Version 1.0.3
MAHMOUDPD/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.