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I think the key part of your question is "as a beginner". For all intents and purposes you can create a state of the art (SoTA) model in various fields with no knowledge of the mathematics what so ever. This means you do not need to understand back-propagation, gradient descent, or even mathematically how each layer works. Respectively you could just ...


2

You could try Mesa. It has various examples that are commonly-used in agent-based modelling, like Epstein's model, a wolf/sheep predator/prey model, and many more. There is also an introductory tutorial.


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What you have on your hands is an attribution problem (and that’s a good keyword to help in your Googling). Two common approaches are computing Shapley values or a Markov chain. In your case, I think Shapley values would be a good approach. To over-simplify, this approach attempts to first determine the total “surplus” of value created by different ...


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Not only is it 100% ok, it's the process. You may be surprised to know that even mathematicians struggle with mathematics, both the proofs they are working on, and the proofs of their colleagues. Some thinkers are so far ahead of the curve, very few understand what they're stating until generations later. The main thing is to keep with it.


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If you want to bee engineer who work with models as black boxes it could be OK. If you want to be researcher, as the job position or for better understanding of the subject it's not OK. Backporpagation is just basic multivariate calculus. If you straggling with it things like Hessians, regularizers, stochastic processes etc. would cause even more problems. ...


1

This is supposed to be a comment but I haven't got enough reputation to do that. In addition to what @the complexitytheorist has said, I recommend you to have a deeper look at your data first, using dimension reduction and visualisation methods such as PCA and t-SNE. A better understanding of data may always save you a lot of work. Then you can choose ...


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I would recommend to have a look at Finding Groups in Data, which is a very readable introduction to clustering methods. It gives a good overview over a number of different algorithms, both agglomerative and hierarchical. As far as I remember, source code for the various algorithms is available on the web somewhere. I am sure you will find a fitting ...


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I would go with a NEAT algorithm for this one. Basically, you "breed" the right network that does the best every generation. Then Rinse and repeat. Doesn't require a training datbase. Bots compete and evolve as they go. For example, this demonstration shows how Neataptic.js teaches a bunch of bots to follow a target: https://wagenaartje.github.io/neataptic/...


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It depends on the complexity on your sentences. If you have a limited range, you could do simple pattern matching on part-of-speech tags. Put your sentence through a tagger (there are plenty of them around) and look for the first noun following a verb: I want an apple Pronoun verb determiner noun (I assume you mean the object, as the subject ...


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