Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente Prochaine révisionLes deux révisions suivantes |
teaching:methcalchim:start [2021/01/22 12:41] – villersd | teaching:methcalchim:start [2021/05/21 10:05] – villersd |
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* special matrix require special algorithms : tridiagonal matrix algorithm (Thomas algorithm) | * special matrix require special algorithms : tridiagonal matrix algorithm (Thomas algorithm) |
* Root findings | * Root findings |
| * Bisection method (dichotomy) : simple and robust algorithm, invariant loop, slow convergence |
| * iterative transformation x = f(x), convergence and divergence situations |
| * secant and regula falsi methods, Convergence Criterion of the Fixed Point Method |
| * Newton-Raphson method (use of derivatives), quadratic convergence, failure, tolerance and stop condition |
| * Van Wijngaardeb-Dekker-Brent method ("black box" in numerical packages) |
| * Roots of polynomials and Bairstow's method |
* Numerical intégration | * Numerical intégration |
| * Equally Spaced methods (trapezoidal, Simpson), accuracy, error dependance,... |
| * Gaussian Quadratures and orthogonal polynomials (special integrals, scale transformations, error estimates,...) |
</note> | </note> |
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===== References ===== | ===== References ===== |
| * 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 |
* Bioinformatics | * Bioinformatics |
* [[http://biopython.org/|Biopython]] | * [[http://biopython.org/|Biopython]] |
* chemistry | * chemistry |
* misc docs : | * misc docs : |
* [[https://pdfs.semanticscholar.org/3161/89e419212794e74bf83442f65de96b5320ba.pdf|Extraction of chemical | * [[https://pdfs.semanticscholar.org/3161/89e419212794e74bf83442f65de96b5320ba.pdf|Extraction of chemical structures and reactions from the literature]] |
structures and reactions from the literature]] | * Blog articles |
| * [[https://py.plainenglish.io/linear-regression-in-plain-python-3b20bb56b31d|Simple Linear Regression Explained With Python - Explained in details which are easy to understand]] Arjan de Haan, Medium, Jan 23 (+ [[https://github.com/Vepnar/Script-Collection/blob/master/linear-regression/Linear%20regression.ipynb|jupyter notebook]]) |
| * simpy : [[https://towardsdatascience.com/simulate-real-life-events-in-python-using-simpy-e6d9152a102f|Simulate Real-life Events in Python Using SimPy]] by Khuyen Tran, May, 2021, Medium, Towards Data Science |
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* [[https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8|Building A Logistic Regression in Python, Step by Step]] | * [[https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8|Building A Logistic Regression in Python, Step by Step]] |
* [[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 |
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