Life & fun · December 19, 2017
How to build your own Multilayer Perceptron with Golang
How to build your own Multilayer Perceptron with Golang launched on December 19, 2017 with 7 votes, #1,131 of 2,482 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 8 years on.
What it does
We can date the birth of artificial neural networks in 1958, with the introduction of Perceptron 1 by Frank Rosenblatt. It was the first algorithm created to reproduce the biological neuron. Conceptually, the easier perceptron that you might think of is made of a single neuron: when it’s exposed to a stimulus, it provides a binary response, just as would a biological neuron. This model differs greatly from the neural network involving billions of neurons in a biological brain. Shortly after his birth, the researchers showed the world the problems of Perceptron: in fact, it was quickly proved that perceptrons could not be trained to recognize many classes of input patterns. To get a more…from made2591.github.io
Does a similar job
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A perceptron with stochastic gradient descent in Go2017 · github.com · ▲94A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go
Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No trainingMar 2026 · github.com · ▲265I replicated David Ng's RYS method (https://dnhkng.github.io/posts/rys/) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76…
A basic generic multi layer perceptron2014 · github.com · ▲8
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