playground VS LunarVim

Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good.

For example, take the circle dataset here: https://playground.tensorflow.org

That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by radius would do the trick. Now try doing that in 1000 dimensions. Sometimes you can, sometimes you can't or do want to bother.

For visualisation and some fun: http://playground.tensorflow.org/

https://seeing-theory.brown.edu/

https://www.3blue1brown.com/

https://playground.tensorflow.org/

There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks:

https://mlu-explain.github.io/neural-networks/

See also here:

http://playground.tensorflow.org/

This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in.

I don’t know much experimenting you’ve done, but many repeated small scale experiments might give you a better intuition at least. I highly recommend this online tool for playing with different environmental variables, even if you’re comfortable coding up your own experiments: http://playground.tensorflow.org

Even if you can’t code, play around with this tool: https://playground.tensorflow.org — you can adjust the shape of the NN and watch how well it classifies the data. Model size obviously matters.

I don't think so. You can easily play around in the browser, using Javascript, or on https://processing.org/, https://playground.tensorflow.org/, https://scratch.mit.edu/, etc.

If anything the problem is that today's kids have too many options. And sure, some are commercial.

Well there is no point of using a multilayer linear neural network, because a cascade of linear transformations can be reduced to a single linear transformation. So you can only approximate linear functions. However if you have prior knowledge about the non linearity of your data lets say you know that it is a linear combination of polynomials up to certain degree, you can expand your input space by explicitly making non linear transformation. For instance a 1D linear regression can be modeled by 2 input neurons and 1 output neuron where the activation of the output is the identity. The input neuron x0 will take a constant input namely 1 and the second input neuron x1 will takes your data x. The output neuron will be y=w_0 * 1+w_1 *x which is equal to y=w_0 +w_1 * x. Let us say that your data follows a polynomial form, the idea is to add input neurons and expand your input to for instance X=[1 x x2] in this case you have 3 input neurons where the third is an explict non linear form of the input so y=w_0 + w_1 x +w_2 x2. The general idea is to find a space where the problem becomes linear. In real life example these spaces are non trivial the power of neural network is that they can find by optimization such space without explicitly encoding these non linearities. Try playing around with https://playground.tensorflow.org/ you can get an intuition about your question.

LunarVim

I would suggest to start getting into vim by first trying out popular vim keybinding plugins available on your favorite code editor and get used to those first. Then, if you want to dive deeper into the power of Neovim, try out popular configs like LazyVim, LunarVim, NvChad... Taking Neovim from a mere text editor to a full-featured IDE with features like intellisense, debugging, testing, etc... on your own takes quite a lot of work and configuration.

I used Helix for a while due to its support for LSP out-of-the-box, which my Vim config at the time couldn't live up to. I switched back to NeoVim after finding LunarVim[1] which had everything I was trying to get setup in my own config.

[1] https://www.lunarvim.org/

I'll admit I didn't look into it, but Helix sounds like something like LunarVim (https://www.lunarvim.org/)

Personally I much prefer that the editor NOT ship with something like that by default, especially when it's so easy to set up. I have several different vim config I use, including a pretty bare-bones one for headless systems, and I much prefer the ability to customize something very specifically.

Build tools that can compose together, rather than a single do-it-all tool. That is the power of the low level editors vs IDE's.

I can't comment on any implementation details, but at least with LunarVim (which I use for daily coding), a slowdown when interacting with LSP is very noticeable. Some others have attested to this on a GitHub issue.

I'm not doubting your experiences with the lack of a slowdown, but there is truth that others do experience it. That might be more of a problem with LunarVim itself rather than Vim, but how likely am I (as someone who would like to avoid what he calls "config hell") or other newcomers to avoid whatever pitfalls there are, if a distribution designed for ease of use by people who know better fall into them?

https://github.com/LunarVim/LunarVim/discussions/3359

Anyways, although i have not used them, LazyVim and LunarVim comes highly recommended. You can try these and see what suits you .