Участник:StasFomin/Bookmarks/Algorithms — различия между версиями

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(Добавлена закладка iutzeler/refresher-course: Jupyter Notebooks for practical sessions of the "Refresher Course in Matrix Analysis and Numerical Optimization" at Université Grenoble Alpes)
(Добавлена закладка Simulating Mass Casualty Triage For Mercy-Class Ships in Python | by Jessi Lanum | Medium)
 
(не показано 39 промежуточных версий этого же участника)
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== 2024 ==
 
== 2024 ==
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=== 2024-12 ===
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* 2024-12-26, 01:39:10: [https://medium.com/@jessilanum/simulating-mass-casualty-triage-for-mercy-class-ships-2d95d374e22d Simulating Mass Casualty Triage For Mercy-Class Ships in Python | by Jessi Lanum | Medium]
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* 2024-12-26, 01:39:00: [https://github.com/itsalljokay/MassCasualty itsalljokay/MassCasualty: Mass Casualty Triage Simulation For Mercy Class Ships]
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=== 2024-05 ===
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* 2024-05-31, 00:40:21: [https://github.com/coder/code-server/issues/388 Add support for live share · Issue #388 · coder/code-server · GitHub]
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*: <html>Just want to say that I am currently able to download and install the .vsix, and Live Share (1.0.5857) works for me on code-server (4.11.0).</html>
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* 2024-05-21, 21:45:24: [https://habr.com/ru/companies/alfa/articles/748824/ Единая нейросетевая модель кредитного скоринга / Хабр]
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=== 2024-04 ===
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* 2024-04-18, 03:27:43: [https://arxiv.org/abs/2104.00480 (2104.00480) Idris 2: Quantitative Type Theory in Practice]
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=== 2024-03 ===
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* 2024-03-28, 16:34:00: [https://docs.google.com/spreadsheets/d/1ioHOHnPrcj6qBrsxotYVeVJVlkqHj2GD/edit?pli=1#gid=1316239506 статус курсы.xlsx - Google Sheets]
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* 2024-03-20, 19:14:22: [https://github.com/sheryl-ai/MVGCN sheryl-ai/MVGCN: Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease (AMIA 2018)]
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* 2024-03-03, 19:22:07: [https://sysblok.ru/knowhow/kak-ustroena-nejroset-bert-ot-google/ Как устроена нейросеть BERT от Google - Системный Блокъ]
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* 2024-03-03, 19:13:07: [https://github.com/LeorFinkelberg/Optimization LeorFinkelberg/Optimization]
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=== 2024-02 ===
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* 2024-02-27, 20:55:59: [https://vk.com/video-176869000_456239030 Video005 — Видео]
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=== 2024-01 ===
 
=== 2024-01 ===
  
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* 2024-01-27, 07:22:19: [https://github.com/MykolaHerasymovych/Optimizing-Acceptance-Threshold-in-Credit-Scoring-using-Reinforcement-Learning/blob/master/Source/simulation.py Optimizing-Acceptance-Threshold-in-Credit-Scoring-using-Reinforcement-Learning/Source/simulation.py at master · MykolaHerasymovych/Optimizing-Acceptance-Threshold-in-Credit-Scoring-using-Reinforcement-Learning]
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* 2024-01-27, 07:05:06: [https://habr.com/ru/companies/glowbyte/articles/519382/ ML и DS оттенки кредитного риск-менеджмента / Хабр]
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* 2024-01-27, 02:47:46: [https://bijournal.hse.ru/data/2015/10/12/1076342406/2.pdf BI 3 (33) 2015 new.indd - 2.pdf]
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* 2024-01-26, 03:06:01: [https://habr.com/ru/companies/yandex/articles/263991/ Вероятностное программирование / Хабр]
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* 2024-01-21, 03:00:34: [https://github.com/fast-algos/graphs-matrices-optimization/blob/master/lecture1.pdf graphs-matrices-optimization/lecture1.pdf at master · fast-algos/graphs-matrices-optimization]
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* 2024-01-21, 02:53:24: [https://github.com/ChrisTX/lecture-notes/tree/7726d59c6caeaa7ac874cbc1fb1e08353bfd98f3/Approximation%20Algorithms lecture-notes/Approximation Algorithms at 7726d59c6caeaa7ac874cbc1fb1e08353bfd98f3 · ChrisTX/lecture-notes]
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* 2024-01-20, 00:29:21: [https://github.com/tlkahn/zkSNARKs-Cryptography-Protocol-Survey tlkahn/zkSNARKs-Cryptography-Protocol-Survey]
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* 2024-01-20, 00:23:51: [https://github.com/prokls/theoretical-computer-science-2/blob/master/randomized_complexity_classes.pdf theoretical-computer-science-2/randomized_complexity_classes.pdf at master · prokls/theoretical-computer-science-2]
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* 2024-01-19, 23:12:56: [https://zhuanlan.zhihu.com/p/37270132 Теорема PCP, часть 1: Введение - Чжиху]
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* 2024-01-19, 23:07:56: [https://github.com/mourad1081/complexity-exam-questions mourad1081/complexity-exam-questions: Réponse aux questions de complexité]
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* 2024-01-19, 23:06:42: [https://github.com/LucaCappelletti94/lectures-notes/tree/0b0550ee36eab19d0d48f74e407beee1e5a3e78f/Unimi/Algoritmi%20e%20Complessita lectures-notes/Unimi/Algoritmi e Complessita at 0b0550ee36eab19d0d48f74e407beee1e5a3e78f · LucaCappelletti94/lectures-notes]
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* 2024-01-19, 22:51:02: [https://github.com/BrownAppliedCryptography/notes BrownAppliedCryptography/notes: Scribed course notes.]
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* 2024-01-19, 22:49:27: [https://github.com/mnielsen/expander_graph_notes mnielsen/expander_graph_notes: Notes on expander graphs]
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* 2024-01-19, 22:48:32: [https://github.com/pragmaticTNT pragmaticTNT]
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* 2024-01-19, 22:43:13: [https://github.com/mnielsen/expander_graph_notes/blob/193880dde69c1aa2bdf974a4975a0b960a278575/expanders_github.tex expander_graph_notes/expanders_github.tex at 193880dde69c1aa2bdf974a4975a0b960a278575 · mnielsen/expander_graph_notes]
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* 2024-01-19, 02:36:35: [https://github.com/sleepymalc/Notes/blob/7e28b1c548da109b7e89a62acc6ed1076841352c/EECS598-001-Approximation-Algorithms-and-Hardness-of-Approximation/Lectures/lec_2.tex Notes/EECS598-001-Approximation-Algorithms-and-Hardness-of-Approximation/Lectures/lec_2.tex at 7e28b1c548da109b7e89a62acc6ed1076841352c · sleepymalc/Notes]
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* 2024-01-19, 02:16:16: [https://github.com/sleepymalc/Notes/tree/7e28b1c548da109b7e89a62acc6ed1076841352c/EECS598-001-Approximation-Algorithms-and-Hardness-of-Approximation Notes/EECS598-001-Approximation-Algorithms-and-Hardness-of-Approximation at 7e28b1c548da109b7e89a62acc6ed1076841352c · sleepymalc/Notes]
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* 2024-01-19, 01:41:18: [https://github.com/arielgabizon/Lectures/blob/master/4elemproofAarhus2019.pdf Lectures/4elemproofAarhus2019.pdf at master · arielgabizon/Lectures]
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* 2024-01-19, 01:10:29: [https://github.com/Landarzar/complexityPoster/blob/master/poster.pdf complexityPoster/poster.pdf at master · Landarzar/complexityPoster]
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* 2024-01-19, 00:54:25: [https://github.com/jarrodmillman/pcp/blob/master/final.tex pcp/final.tex at master · jarrodmillman/pcp]
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* 2024-01-18, 22:17:49: [https://github.com/SanskarX10/Coding-Graph-Theory-101/tree/main SanskarX10/Coding-Graph-Theory-101: Coding and explanation of Graph theory algorithms used in computer science in python-3 . These set of notebooks acts as a course in graph theory.]
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* 2024-01-18, 22:16:14: [https://github.com/rjwrobel86/Python4Statistics/tree/main rjwrobel86/Python4Statistics: Python Scripts and Jupyter Notebooks for Prof. Robert Wrobel's Applied Business Statistics course at Webster University and anybody else who wants to learn!]
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* 2024-01-18, 22:04:34: [https://github.com/hishamcse/Discrete-Math-Specialization-Coursera-/tree/master hishamcse/Discrete-Math-Specialization-Coursera-: This repository contains the materials, python codes for the quiz and some codes implemented using jupyter notebook related to the specialization ( Introduction to Discrete Mathematics In Computer Science)]
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* 2024-01-18, 21:52:09: [https://github.com/i40a/python-for-industry40 i40a/python-for-industry40: Jupyter Notebook files from the course Python for Industry 4.0.]
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* 2024-01-18, 21:50:24: [https://github.com/kylebeggs/Numerical-Methods-in-Python kylebeggs/Numerical-Methods-in-Python: A collection of Python notebooks covering topics in an undergraduate Numerical Methods in Engineering course.]
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* 2024-01-18, 21:49:05: [https://github.com/sslotin/universum-dl sslotin/universum-dl: Some notebooks from a Deep Learning mini-course taught in Summer '18]
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* 2024-01-18, 21:43:06: [https://github.com/ali-tny/boyd-convex-optimisation-py ali-tny/boyd-convex-optimisation-py: homework notebooks for the Stanford Convex Optimisation course in python (rather than Matlab)]
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* 2024-01-18, 21:40:45: [https://github.com/Novota15/fourier-transforms-in-python Novota15/fourier-transforms-in-python: A jupyter python notebook that provides a crash course on Fourier Series, Fourier Transforms, Fast Fourier Transforms, and improving Chebyshev Interpolation with FFT]
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* 2024-01-18, 21:37:53: [https://github.com/nivkeren/computational-methods nivkeren/computational-methods: computational-methods course notebooks]
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* 2024-01-18, 21:36:24: [https://github.com/ptah23/audio-transformers-course-notebooks ptah23/audio-transformers-course-notebooks: Python notebooks for hugging face audio transformers course]
 
* 2024-01-18, 21:31:16: [https://github.com/iutzeler/refresher-course iutzeler/refresher-course: Jupyter Notebooks for practical sessions of the "Refresher Course in Matrix Analysis and Numerical Optimization" at Université Grenoble Alpes]
 
* 2024-01-18, 21:31:16: [https://github.com/iutzeler/refresher-course iutzeler/refresher-course: Jupyter Notebooks for practical sessions of the "Refresher Course in Matrix Analysis and Numerical Optimization" at Université Grenoble Alpes]
 
* 2024-01-18, 21:30:20: [https://github.com/codeninja55/mathematic_notebooks/tree/master codeninja55/mathematic_notebooks: A repository store for all mathematic references, jupyter notebooks, and notes from various courses taken.]
 
* 2024-01-18, 21:30:20: [https://github.com/codeninja55/mathematic_notebooks/tree/master codeninja55/mathematic_notebooks: A repository store for all mathematic references, jupyter notebooks, and notes from various courses taken.]

Текущая версия на 01:39, 26 декабря 2024

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  • 2022-04-27, 22:04:58: Nick Titterton
    X = cvx.Variable((V, V), PSD=True)

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  • 2021-05-30, 11:24:30: Facebook
    Аллен Дауни прямо радует - читается хорошо, без академической воды и понятно, с адекватными и ясными примерами практических задач. Последний раз все было так ясно и лаконично при перерешивании задач по терверу из советского учебника Вентцель и книги по байесовским методам Джона Крушке. Покрутил, наверное, в 10 раз в голове теорему Байеса и, вообще, понятие вероятности, условной вероятности, совместной вероятности, априорного и апостериорного распределения, сопряженного приора, pdf, pmf, cdf с разных сторон (и в очередной раз так и не просек простую идею бета-распределения, но, верю, она же есть) - ну чтобы чуйка развилась еще больше. Я честно, от всего сердца и ума, делал несколько подходов к прикладной байесовской статистике с разных сторон и с разными инструментами, прочитал, наверно несколько книг (поняв в них далеко не все) и не помню уже как много статей, но постоянно преследовал вопрос - а зачем и как это мне поможет в повседневной практике? Основная цель, которую я преследовал и до сих пор преследую для себя - научиться понимать "небольшие" данные и причины, стоящие за ними глубже, чем позволяют популярные статистические методы и мало кем, на самом деле, глубоко понимаемые доверительные интервалы на хи-квадратах, погоняемых группами сТЬЮдентов

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