Hi new to the forums and to AI in general (I have a more theoretical math background in electrical engineering). Sorry if the post is a bit long, I wanted to make sure I explained myself fully, and english is not my first language Hopefully some of you can bear with me, and be able to help.
I have a specific project in mind I wish to do using AI, but I’m unsure which method to use, and where to focus my time in terms of the different courses and lessons available (ML / DL / etc.). The details are field-specific, so I’ll just describe what I’m trying to achieve in general.
I have sampled points on a general tensor, and the goal is to find a mapping function of these points that achieves good results in terms of a criteria I set it (MSE), under a certain constraint. For the sake of simplicity, the criteria is MSE, and the constraint is some sort of upper bound limitation on the mapping.
Everything is continuous of course, the sampled points and their mapping.
I’ve already built a program that does this using scipy.optimize.minimize, which uses an iterative method for constrained nonlinear optimization. This method is very similar to the linear regression Jeremy demonstrated in lesson 2 of DP.
I want to try to use and test the performance of a neural network, or random forest, or something along those lines, but I got a bit lost in the videos I started watching, and I did not find a suitable example that was close to what I’m trying to achieve.
So first of all, I’d like to ask which approach you guys think would suit my problem (RF? DL? etc.), and if you have any tips on which videos / topics are relevant, I’d greatly appreciate it.
Thank you very much in advance to anyone who replies