playground VS processing

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.

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I disagree. There are so many creative tools that are now online that you can access from your browser that were not envisioned in the original web. It is obviously true that not EVERY website is about creation (but to expect that seems unreasonable?), but even Wikipedia is a collaborative project.

Examples include products from big vendors like Adobe's Photoshop, to smaller products like SketchUp, to more indy generative art tools like https://processing.org and Strudel (https://news.ycombinator.com/item?id=39924210).

Would processing[0] be a good fit? It's designed to be easy to use and learn but powerful enough for professional use. Very quick to get cool stuff moving on a screen and the syntax is Java with a streamlined editing environment.

[0] https://processing.org/

I would personally use the language Processing. It's the one I use the most. And it's relatively easy to start drawing text, squares, and do other kinds of things. (It's kind of like java, but without all the boilerplate code)

Processing (P5) had this: you can select any string of text in its IDE anl search for it in the docs, and if it's one of the built-in functions or constants it will open the associated static html page that came installed with the software, so no internet nor server required. And despite being offline you can still navigate the docs too. This feels a lost basic skill in static site generation these days.

It was the only creative coding framework that had complete, offline documentation like that at the time I might add. OpenFrameworks is still mostly autogenerated stubs for example.

IMO it was one of the things that gave Processing an edge in educational contexts over all alternatives. I was pretty sad to see p5.js not fully continue that tradition and require that you go online to read the docs, and that it's not a static website but that text is rendered with javascript when you open it (still complete and with examples though).

https://processing.org/

https://p5js.org/

Processing is very cool, especially if you like graphics.

https://processing.org/

Processing is a flexible software sketchbook and a language for learning how to code. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. There are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning and prototyping.

And it's not even their IDE. They just slapped some AVR compilers into Processing

https://processing.org/