| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente |
| teaching:methcalchim:start [2021/05/21 16:03] – [References] villersd | teaching:methcalchim:start [2023/04/12 10:08] (Version actuelle) – [Classical numerical methods] villersd |
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| + discussion of some approximations like [[wp>Bhaskara_I's_sine_approximation_formula|Bhaskara I's sine approximation formula]] | + discussion of some approximations like [[wp>Bhaskara_I's_sine_approximation_formula|Bhaskara I's sine approximation formula]] |
| * [[https://twitter.com/fermatslibrary/status/1267450081151782913]] | * [[https://twitter.com/fermatslibrary/status/1267450081151782913]] |
| | * [[https://medium.com/@mathcube7/chebyshev-interpolation-with-python-2f2e89bb7c30|Chebyshev Interpolation With Python]] Mathcube, Medium, 03/02/2023 |
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| === Molecules modelisation and visualization === | === Molecules modelisation and visualization === |
| * Digital image processing, image recognition | * Digital image processing, image recognition |
| * particle tracking,... | * particle tracking,... |
| | * Voronoi diagrams, Delaunay triangulation,... ([[https://towardsdatascience.com/the-fascinating-world-of-voronoi-diagrams-da8fc700fa1b|ref1]]) |
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| * Gradient descent optimization | * Gradient descent optimization |
| * [[https://machinelearningmastery.com/gradient-descent-optimization-from-scratch/|How to Implement Gradient Descent Optimization from Scratch]] By Jason Brownlee on April 16, 2021 | * [[https://machinelearningmastery.com/gradient-descent-optimization-from-scratch/|How to Implement Gradient Descent Optimization from Scratch]] By Jason Brownlee on April 16, 2021 |
| | * [[https://towardsdatascience.com/implementing-gradient-descent-in-python-from-scratch-760a8556c31f|Implementing Gradient Descent in Python from Scratch]] Vatsal Sheth, Medium, 18/02/2022 |
| * Bioinformatics | * Bioinformatics |
| * [[http://biopython.org/|Biopython]] | * [[http://biopython.org/|Biopython]] |
| * K-Means | * K-Means |
| * [[http://benalexkeen.com/k-means-clustering-in-python/|K-means Clustering in Python]] | * [[http://benalexkeen.com/k-means-clustering-in-python/|K-means Clustering in Python]] |
| | * [[https://medium.com/thrive-in-ai/k-means-clustering-in-python-9f00b3d7abc3|K Means Clustering in Python]] Rohit Raj, Medium, 02/03/2022 |
| | * [[https://medium.com/thrive-in-ai/classification-algorithms-in-python-5f58a7a27b88|Classification Algorithms in Python]] Rohit Raj, Medium, 15/03/2022 |
| * Applications (suggestions, examples,...) | * Applications (suggestions, examples,...) |
| * [[https://scicomp.stackexchange.com/questions/28195/2d-ising-model-in-python|2D Ising Model in Python]] | * [[https://scicomp.stackexchange.com/questions/28195/2d-ising-model-in-python|2D Ising Model in Python]] |
| * Active matter simulations ([[https://arxiv.org/pdf/cond-mat/0611743.pdf|Vicsek]], 1995) : | * Active matter simulations ([[https://arxiv.org/pdf/cond-mat/0611743.pdf|Vicsek]], 1995) : |
| * [[https://medium.com/swlh/create-your-own-active-matter-simulation-with-python-76fce4a53b6f|Create Your Own Active Matter Simulation (With Python)]] Philip Mocz, The Startup, Medium 2021 + [[https://github.com/pmocz/activematter-python]] | * [[https://medium.com/swlh/create-your-own-active-matter-simulation-with-python-76fce4a53b6f|Create Your Own Active Matter Simulation (With Python)]] Philip Mocz, The Startup, Medium 2021 + [[https://github.com/pmocz/activematter-python]] |
| | * Fourier transforms : [[https://towardsdatascience.com/fourier-transforms-an-intuitive-visualisation-ba186c7380ee|Fourier Transforms: An Intuitive Visualisation - An intuitive visualization of discrete Fourier transforms applied to simple time-series data]] Diego Unzueta, Towards Data Science, Medium, 17/09/2021 |
| | * Monte-Carlo simulations : [[Monte Carlo Simulation — a practical guide - A versatile method for parameters estimation. Exemplary implementation in Python programming language]] Robert Kwiatkowski, Medium, 31/01/2022 |
| | * régression logistique : [[https://mlu-explain.github.io/logistic-regression/]] |
| | * [[https://towardsdatascience.com/cubic-splines-the-ultimate-regression-model-bd51a9cf396d|Cubic Splines: The Ultimate Regression Model - Why cubic splines are the best regression model out there]] Brendan Artley, Medium, 27/07/2022 |
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| * [[https://medium.com/swlh/building-linear-regression-in-python-75a429b0d3ba|Building Linear Regression in Python]] | * [[https://medium.com/swlh/building-linear-regression-in-python-75a429b0d3ba|Building Linear Regression in Python]] |
| * [[https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681|17 types of similarity and dissimilarity measures used in data science]] : explains various methods for computing distances and showing their instances in our daily lives. Additionally, it will introduce you to the pydist2 package. Mahmoud Harmouch, Medium, 14/03/2021 | * [[https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681|17 types of similarity and dissimilarity measures used in data science]] : explains various methods for computing distances and showing their instances in our daily lives. Additionally, it will introduce you to the pydist2 package. Mahmoud Harmouch, Medium, 14/03/2021 |
| | * [[http://jakevdp.github.io/|Pythonic Perambulations]] de Jake VanderPlas |
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