[photon_off]

Also, another one-off question:

What if the data I have is relational? Must it all be squashed down? Imagine a family tree: can a "node" simply contain "parents", and the model will figure out "virtual columns", or would I generate this input manually (for thousands of columns), example columns: "parent(0).parent(0).child(0).blood_type", "parent(0).parent(0).child(1).blood_type", etc.

Consequentially... I will definitely have more questions like this. What is a good resource to go off on my own, or with a more suitable audience?

[tlack]

In most cases I've seen, the model lives on its own, with only very surface-level connections with your system. It can't "look up" stuff it needs, such as the query you mention.

There are graph-based systems but I think they're more interested in understanding relationships in the graph itself - grouping like things together, predicting relationships and distances, etc., rather than attributes of the nodes.

I assume you want the system to learn something like "if parent A is type O, and grandparent of parent B is B+, child is more likely to be tall" or similar. I don't think the network can learn things like that in terms of understanding the linkages to predict numbers. It might be able to predict it simply by being given enough examples after "flattening," though, so the functional result is similar.

I've found Reddit's MLQuestions [0] group to be interesting and sometimes accessible for a non-academic ML enthusiast. Some Youtube content can be useful too, but most are just repeating the content of papers. I'm still seeking a real commoner-level message board to discuss this kind of stuff without dumbshaming.

What I've learned most from is downloading example code and actually using it, reading parts of it, and trying to apply it. It's easy to dip your toes into stuff on Google Colab notebooks.

Uber Ludwig [2] is an interesting all-in-one low-code system that lets you try out different ideas quickly. At least in theory. They give an example cases for all the different types of networks they support along with matching YAML to specify the model details. So you can sorta just throw some data in there and build a command line to try ideas, rather than learning a lot about Pytorch, Keras, etc., and potentially introducing subtle bugs.

Email me (addr in profile) if you want to chat more.

[1] http://reddit.com/r/mlquestions/ [2] https://uber.github.io/ludwig/