#java #adjacency #adjacency_matrix #algorithm #algorithms #dijkstra #dynamic_programming #edmonds_karp_algorithm #geometry #graph_theory #linear_algebra #mathematics #matrix_multiplication #maxflow #nlog #search_algorithm #search_algorithms #sorting_algorithms #strings #traveling_salesman #tree_algorithms

https://github.com/williamfiset/Algorithms

#other #2021 #artificial_intelligence #cheat_sheets #course #coursera #coursera_machine_learning #deep_learning #learn_to_code #learning #learning_python #linear_algebra #machine_learning #neural_networks #practice #probability_statistics #read_articles #tutorial #tutorials #youtube #youtube_playlist

https://github.com/louisfb01/start-machine-learning-in-2020

#python #attention #cbam #excitation_networks #linear_layers #paper #pytorch #squeeze #visual_tasks

https://github.com/xmu-xiaoma666/External-Attention-pytorch

#python #attention_mechanism #deep_learning #gpt #gpt_2 #gpt_3 #language_model #linear_attention #lstm #pytorch #rnn #rwkv #transformer #transformers

https://github.com/BlinkDL/RWKV-LM

#typescript #django #docker #jira #jira_alternative #kanban #linear #nextjs #postgresql #project_management #python #react #redis #rest_api

https://github.com/makeplane/plane

#cplusplus #high_performance #interior_point_method #linear_optimization #mixed_integer_programming #parallel #quadratic_programming #simplex

HiGHS is a free, high-performance software that solves large and complex optimization problems like linear, quadratic, and mixed-integer programming. It works fast on many computers, including Linux, MacOS, and Windows, without needing extra software. You can use it through various programming languages like Python, C, C#, and Fortran, making it easy to integrate into your projects. HiGHS supports both serial and parallel computing, and it is advancing GPU acceleration for even faster solutions. This helps you efficiently find the best solutions for planning, scheduling, and decision-making problems in science, engineering, and business. Installation is straightforward, and detailed documentation is available to guide you[1][2][3][4].

https://github.com/ERGO-Code/HiGHS