Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente Prochaine révisionLes deux révisions suivantes |
teaching:methcalchim:start [2018/10/18 10:01] – villersd | teaching:methcalchim:start [2019/12/12 08:46] – [Miscellaneous] villersd |
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=== Linear and non-linear least squares approximations === | === Linear and non-linear least squares approximations === |
Application to deconvolution (Levenberg–Marquardt algorithm) | * Application to deconvolution (Levenberg–Marquardt algorithm) |
| * Reférences : |
| * [[https://pubs.acs.org/doi/abs/10.1021/acs.jchemed.8b00649|An Open-Source, Cross-Platform Resource for Nonlinear Least-Squares Curve Fitting]] Andreas Möglich, J. Chem. Educ., 2018, 95 (12), pp 2273–2278 DOI: 10.1021/acs.jchemed.8b00649 |
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=== Chebyshev approximation === | === Chebyshev approximation === |
| + discussion of some approximations like [[wp>Bhaskara_I's_sine_approximation_formula|Bhaskara I's sine approximation formula]] |
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=== Molecules modelisation and visualization === | === Molecules modelisation and visualization === |
* Machine Learning | * Machine Learning |
* Scikit-learn | * Scikit-learn |
| * [[https://towardsdatascience.com/linear-regression-in-6-lines-of-python-5e1d0cd05b8d|Linear Regression in 6 lines of Python]] (using scikit-learn) |
* [[https://datafloq.com/read/12-algorithms-every-data-scientist-should-know/2024|12 Algorithms Every Data Scientist Should Know]] | * [[https://datafloq.com/read/12-algorithms-every-data-scientist-should-know/2024|12 Algorithms Every Data Scientist Should Know]] |
* Deep Learning | * Deep Learning |
* Published applications | * Published applications |
* [[http://pubs.acs.org/doi/10.1021/acs.jchemed.7b00395|Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications]] Charles J. Weiss, Journal of Chemical Education 2017 94 (12), 1904-1910 DOI: 10.1021/acs.jchemed.7b00395 | * [[http://pubs.acs.org/doi/10.1021/acs.jchemed.7b00395|Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications]] Charles J. Weiss, Journal of Chemical Education 2017 94 (12), 1904-1910 DOI: 10.1021/acs.jchemed.7b00395 |
| * [[https://medium.freecodecamp.org/an-overview-of-the-gradient-descent-algorithm-8645c9e4de1e|An overview of the Gradient Descent algorithm]] |
| * [[https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8|Building A Logistic Regression in Python, Step by Step]] |
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