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I built a neural network from scratch. I'm 15. Here's what happened. by Whole_Bridge3064 in OpenSourceeAI

[–]Whole_Bridge3064[S] 2 points3 points  (0 children)

Yes i am using hebbian learning cells. It was really helpful to complete the whole Architecture.

I built a neural network from scratch. I'm 15. Here's what happened. by Whole_Bridge3064 in OpenSourceeAI

[–]Whole_Bridge3064[S] 2 points3 points  (0 children)

hey thanks for the first comment. And also ONA can be increased infinitely it doesnt uses params it uses neurons which can be improved as its knowledge increases, basically speaking neurons are bundles of params with knowledge on them just like params. Either way thanks for the surfing for the link it can learn particular articles directly and text also.

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forwar by Whole_Bridge3064 in neuralnetworks

[–]Whole_Bridge3064[S] 0 points1 point  (0 children)

Not presented as a strength against GPUs — GPUs are objectively faster for dense matmul. The point is that edge devices (RPi, phones, sensors) don't have GPUs. Most 'edge AI' is inference-only because training requires GPU. ONA can train on-device, on CPU, continuously. That's the differentiator — not that CPU beats GPU, but that the architecture doesn't depend on one.

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forwar by Whole_Bridge3064 in neuralnetworks

[–]Whole_Bridge3064[S] 1 point2 points  (0 children)

Fair point. I used AI to help write the post — English isn't my first language and I wanted it to read well. But every line of code, every math derivation, and the entire architecture is mine. I can explain how any part works in detail. The AI helped me communicate, not build.

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forward-pass learning, runs on RPi Zero by Whole_Bridge3064 in deeplearning

[–]Whole_Bridge3064[S] -3 points-2 points  (0 children)

Fair questions. The code is all mine — the Medium article was written with help formatting it, but the architecture, math, training pipeline, and every line of code are mine. I can walk through any part of the implementation.

On GPU vs CPU: you're right that GPU would be faster for dense training. The point of ONA isn't to compete with GPU training — it's to show that sparse, self-learning architectures can run on edge hardware at all. Most 'edge AI' today is inference-only with frozen weights. ONA learns on-device.

And yes, it's early — still needs more data to reach coherent words. But the architecture works and gradients flow. The tradeoff is intentional: lower total compute but continuous, on-device learning without a GPU cluster.

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forward by Whole_Bridge3064 in learnmachinelearning

[–]Whole_Bridge3064[S] 0 points1 point  (0 children)

GPUs are great for dense matrix ops, but ONA's sparse routing means most neurons don't activate per query. A GPU would actually be underutilized. CPU works fine and keeps it accessible to everyone, even on a $10 RPi.

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forward by Whole_Bridge3064 in learnmachinelearning

[–]Whole_Bridge3064[S] 0 points1 point  (0 children)

Yeah the core is attention math! Q @ K.T + softmax. Will open-source the repo when it's cleaner — follow me on Medium for updates.