The simplest Perceptron you'll ever see, in JavaScript
The simplest Perceptron you'll ever see. Contribute to victorqribeiro/perceptron development by creating an account on GitHub.
The simplest Perceptron you'll ever see, in JavaScript launched on January 24, 2019 with 11 votes, #909 of 2,587 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 7 years on.
In plain words
The Simplest Perceptron is a JavaScript implementation of a basic perceptron, a fundamental machine learning algorithm. It targets developers learning neural networks and machine learning concepts. The project stands out for its simplicity and educational focus, making perceptron concepts accessible to beginners without unnecessary complexity. Available on GitHub, it provides a straightforward starting point for understanding how perceptrons work before moving to more advanced frameworks.
written from the facts on this page · September 2026
Does a similar job
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Perceptron – Artificial Neural Network Builder2017 · github.com · ▲61A flexible artificial neural network builder to analyse performance, and optimise the best model. - GitHub - casparwylie/Perceptron: A flexible artificial neural network builder to analyse performance, and optimise the best model.
Single neuron neural network written in COBOL2020 · github.com · ▲94A perceptron written in COBOL. Contribute to victorqribeiro/perceptronCobol development by creating an account on GitHub.
A perceptron with stochastic gradient descent in Go2017 · github.com · ▲94A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go
- AQA Quantum Perceptron – First Steps in QML2019 · qml.entropicalabs.io · ▲65
- ATA tiny JS library for real-time localization of eye pupils2019 · tehnokv.com · ▲153
Neural networks tutorial series with code2020 · github.com · ▲107The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.
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