If you want to learn the fundamentals of ML I recommend a book, such as Deep Learning: Foundations and Concepts by Chris Bishop. If you insist on staying online, one option is https://course.fast.ai/
If you don't know ML I don't think you're going to learn much through ad hoc demos.
I know you're joking, but if you really wanted to, you can if you have a network of these, since you can build NAND gates from perceptrons. If you have NAND gates, then you can build any other gates from these and then you can build a computer [1].
The IF statement is the root creator of software programming. It has the ability to compare two values against each other and branch out to blocks of instructions. So it is perceiving (reading), decision making and routing - all that which differentiate life from inanimate objects. The AI agents perform the exact same loop, by delegating the first two steps to a model.
Going further backwards, the transistor (or a PNP junction) is the hardware level enabler of the IF statement. The action (switching) driven by the current which in turn controls other switches, is the first manifestation of "observe and act" by inanimate things at the speed of electricity.
Mechanical equivalents existed ofcourse - speed of a governer which controls the flow of fuel which in turn controls the speed of the governer.
In the early days of machine learning (before the first AI winter), networks like this were often implemented and trained in hardware: https://en.wikipedia.org/wiki/ADALINE
That was the first thing that came to mind when I read "the smallest brain you can build". Nowadays, that "small brain" would likely be built on a breadboard using op-amps instead.
I think Karpathy's microgpt blogpost is the best in this genre in a long time, and it also includes a multi layer perceptron. It's a step up in the hierarchy, so reading both is helpful, of course.
I think it should be quite obvious that perceptrons are far from the smallest units that are capable of learning. They store many bytes of information, require a non-local update process, need numeric (i.e. symbolic) inputs and involve relatively complex computations. You can go much simpler. For example:
Is this something that could be scaled up and used, for instance, to recognize features in images?
Or to put it another way are there any local only tools that can be trained on my own set of images to automatically tag new images? Tools that do not already have built in classes of image.
I take a lot of photographs and it would be handy to reduce the drudgery of tagging them so to say broadly what the subject was so that they are easier to find later.
38 comments
[ 1.6 ms ] story [ 49.3 ms ] threadIf you don't know ML I don't think you're going to learn much through ad hoc demos.
(before numarray and numpy merged!)
[1] https://www.nand2tetris.org/
I played with similar approach in JavaScript and built a NanoNeuron https://github.com/trekhleb/nano-neuron (it is more verbose than Python though)
f(x) = 0.
> In 1958, a researcher named Frank Rosenblatt built a machine *he called* the perceptron.
> It was *inspired* by a single brain cell, a neuron.
Going further backwards, the transistor (or a PNP junction) is the hardware level enabler of the IF statement. The action (switching) driven by the current which in turn controls other switches, is the first manifestation of "observe and act" by inanimate things at the speed of electricity.
Mechanical equivalents existed ofcourse - speed of a governer which controls the flow of fuel which in turn controls the speed of the governer.
That was the first thing that came to mind when I read "the smallest brain you can build". Nowadays, that "small brain" would likely be built on a breadboard using op-amps instead.
https://karpathy.github.io/2026/02/12/microgpt/
https://medium.com/@VictorBanev/the-simplest-learning-machin...
This is a description of a 5-line algorithm that learns and stores approximate probability of an event using just 1 byte of persistent memory.
Or to put it another way are there any local only tools that can be trained on my own set of images to automatically tag new images? Tools that do not already have built in classes of image.
I take a lot of photographs and it would be handy to reduce the drudgery of tagging them so to say broadly what the subject was so that they are easier to find later.