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import Chart from 'https://esm.run/chart.js/auto';Analytics
import * as d3 from 'https://esm.run/d3';
let my_data = [1,2,3]
let sum = d3.sum(my_data)
return sumLast updated
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import Chart from 'https://esm.run/chart.js/auto';import * as d3 from 'https://esm.run/d3';
let my_data = [1,2,3]
let sum = d3.sum(my_data)
return sumLast updated
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import * as brain from 'https://esm.run/brain.js';
// provide optional config object (or undefined). Defaults shown.
const config = {
binaryThresh: 0.5,
hiddenLayers: [3], // array of ints for the sizes of the hidden layers in the network
activation: 'sigmoid', // supported activation types: ['sigmoid', 'relu', 'leaky-relu', 'tanh'],
leakyReluAlpha: 0.01, // supported for activation type 'leaky-relu'
};
// create a simple feed-forward neural network with backpropagation
const net = new brain.NeuralNetwork(config);
await net.train([
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] },
]);
const output = net.run([1, 0]); // [0.987]
return output[0]