combobulate VS playground

Eh, I've been looking and haven't found anything for other editors that actually tries to use TreeSitter for anything beyond highlighting. The Emacs structural editing packages are still very WIP but at least they exist.

(And also some have been based on the out of tree implementation that's been around for a while now)

Example: https://github.com/mickeynp/combobulate

Looking at https://github.com/mickeynp/combobulate/blob/master/combobulate-python.el, it at the very least delegates to python-indent-calculate-levels, so the logic is mixed.

I went from an old config rich setups from before lsp's to lsp-mode ones etc... Right now I would say that eglot + pylsp gives you the best experience, you can use pyenv and pyvenv mode to manage your virtual environments. Now that treesitter is also being used you can try out https://github.com/mickeynp/combobulate

I think the following packages would fit your wishlist, as it is very similar to mine. As mentioned in the replies, there is (https://github.com/magnars/expand-region.el) and (https://github.com/mickeynp/combobulate). I regularly use (https://github.com/Fuco1/smartparens).

re syntactic text objects: https://github.com/mickeynp/combobulate

I haven't used it, but based on the description, it looks like combobulate would be an example of this:

from github README.rst "Emacs package that provides a standardized framework for manipulating and navigating your source code using tree sitter's concrete syntax tree " -> https://github.com/mickeynp/combobulate

https://www.spacemacs.org/ with https://github.com/emacs-tree-sitter/elisp-tree-sitter then write a iterator/loop query for language(s) editing per https://tree-sitter.github.io/tree-sitter/syntax-highlightin...

tad less installation heavy (sorta) but also makes use of tree-sitter syntax queries : https://www.lunarvim.org (neovim with treesitter syntax)

blockman usage examples: https://www.youtube.com/channel/UC5539gDeAdWqeXcczWuhnBA

Alternative examples / takes (per user interface):

### embedding a block of source code in a document:

  ** carrotsearch.gethub.io/apidocs/code-blocks

I don't know what this fork brings to table, but you could try tree-sitter today with your vanilla Emacs using a package[1] that works via dynamic module.

Personally I am more interested in getting structural selection and navigation reliably working for any language. There is also a package named combobulate[2] to help with that.

[1] https://emacs-tree-sitter.github.io/

[2] https://github.com/mickeynp/combobulate

TIL https://github.com/mickeynp/combobulate Thank you, @snafuchs !

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.