Wit the raise of AI and ML techniques, it's now easier than before to experimentally test maths algorithms,
In my recent discussion with Michael Jagdeo, he was able to make a quick thesis comparing my "improved secant method" with the standard "secant method" using ML techniques and create a thesis about it in less than 15 minutes.
The link to it : https://www.kaggle.com/code/exponentlabs/rhafli-experiment
What do you think guys?
Dear @Marouane Rhafli,
Machine learning experiments help you discover the most optimum model version for your specific use case. Read this article to learn about different types of experiments and what you need to watch out for when conducting them.
In science, an experiment is a question to nature about a scientific theory. For example, Arthur Eddington’s famous 1919 experiment verified Einstein’s relativity theory by asking the question—does light bend due to the gravitational field produced by the sun? To answer the question, he recorded the perceived position of stars during a solar eclipse when the sun’s light was blocked. He then compared the measured star positions to their expected positions. Eddington’s readings showed that the sun’s gravitational field does indeed bend light, providing strong support for the relativity theory.
https://nebius.com/blog/posts/machine-learning-experiments
Regards,
Shafagat
Be careful. The design of informative experiments is a non-trivial task. In your case, the use of the test function with a quite small gradient in a wide range of the root would probably provide different conclusions.
Miroslav Karny yes I know, the purpose ain't the result itself but the way to use ML techniques for experimental tests, and in a quick time