npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

jellybrain

v1.0.3

Published

A simple artificial neural network

Downloads

4

Readme

JellyBrain

JellyBrain is a simple neural network written in Javascript. This was written as an exercise to learn how neural networks work.

Simple Usage

const JellyBrain = require('../JellyBrain.js');

let brain = new JellyBrain(2, 2, 1);    // 2 inputs, 2 hidden nodes, 1 output

brain.train([0.2, 0.5], 1);
brain.guess([0.1, 0.6]);

Advanced Usage

It is possible to use a variety of cost functions and activation functions. It is also possible to train in batches, although the implementation avoids using tensors to simplify things.

const {JellyBrain, costFuncs, activationFuncs} = require('../JellyBrain.js');

// example parameters for training on the mnist dataset
let brain = new JellyBrain(784, 784, 10, costFuncs.crossEntropy, 0.001, activationFuncs.sigmoid, activationFuncs.softmax);
let simpleBrain = new JellyBrain(2, 2, 1);

simpleBrain.addToBatch([0.2, 0.5], 1);
simpleBrain.addToBatch([0.6, 0.4], 0.7);
simpleBrain.addToBatch([0.1, 0.2], 0.2);
simpleBrain.computeBatch();
simpleBrain.guess([0.1, 0.6]);

License

ISC