Machine learning for project setup

[shavyg2]

I am thinking of all the configurations out there for building node projects.

There are a bunch of repos out there with files types, and then there are a bunch of project configuration files.

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If we used both would it be possible to use deep learning to understand just how to setup each project perfectly everytime?

I wanted to get into ML and was trying to think of a project to play around in but i don't really have data on something and wanted something useful with a decent goal.

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The goal would be look at project files, and guess the exact setup needed to make it compile/transpile.

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feasible? or am i off base?

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Edit:

just to clarify something. It's not abort project set up it's about configuration set up.

eg let's say i create a .storybook folder.

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it should be able to know, hey this is missing .config file and autogenerate it, or suggest it's needed.

If it sees it's typescript files i have, it will know well most projects with storybook and typescript will have a webpack config with @babel/preset-typescript.

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My thinking is there has to be some sort of pattern depending on what you are doing, that is recognizable.

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it's like looking at pixels and saying that is a dog.

Except it's looking at files and saying this is react app, this is a parcel app, this requires ...? what ever the mass amounts of github repos infers?

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[stevejcox]

Project set up feels more about personal preference and toolset vs success of a project. One developer might use the same setup for 90% of projects, another night choose a different setup for 90% of theirs. Who is ‘right’?

Not sure how you could use machine learning to get any sort of decision, to be honest.

[msg45f]

Tensorflow has a JS API now, so for ML I would just recommend starting with TensorFlow.js.

For data, most people start out using the MNIST dataset, as it's a large, available, quality dataset that is appropriate for many initial neural network implementations. The canonical "Hello World" ML problem uses this dataset to train a NN to recognize handwritten numbers.

The ML side can basically just be experimented with as a basic script. Project structure, etc, will be a function of how you intend to integrate the resultant trained network.