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Steffo 2020-06-15 18:47:56 +02:00
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<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><title>Appunti Web</title><meta name="viewport" content="width=device-width,initial-scale=1"><meta name="mobile-web-app-capable" content="yes"><meta name="apple-mobile-web-app-capable" content="yes"><link rel="apple-touch-icon" href="./assets/icons/apple-touch-icon.png"><link rel="manifest" href="/manifest.json"><meta name="theme-color" content="#a0ccff"><link rel="shortcut icon" href="/favicon.ico"><style>*{box-sizing:border-box}body{background-color:#0d193b;color:#a0ccff;font-family:Arial,sans-serif}</style><link href="/bundle.0c633.css" rel="preload" as="style" onload="this.rel='stylesheet'"><noscript><link rel="stylesheet" href="/bundle.0c633.css"></noscript></head><body><script type="__PREACT_CLI_DATA__">{"preRenderData":{"url":"/"}}</script><script nomodule="">!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()},!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script><script crossorigin="anonymous" src="/bundle.bde4a.esm.js" type="module"></script><script nomodule="" src="/polyfills.d1990.js"></script><script nomodule="" defer="defer" src="/bundle.9f3db.js"></script></body></html>
<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><title>Appunti Web</title><meta name="viewport" content="width=device-width,initial-scale=1"><meta name="mobile-web-app-capable" content="yes"><meta name="apple-mobile-web-app-capable" content="yes"><link rel="apple-touch-icon" href="./assets/icons/apple-touch-icon.png"><link rel="manifest" href="/manifest.json"><meta name="theme-color" content="#a0ccff"><link rel="shortcut icon" href="/favicon.ico"><style>*{box-sizing:border-box}body{background-color:#0d193b;color:#a0ccff;font-family:Arial,sans-serif}</style><link href="/bundle.0c633.css" rel="preload" as="style" onload="this.rel='stylesheet'"><noscript><link rel="stylesheet" href="/bundle.0c633.css"></noscript></head><body><script type="__PREACT_CLI_DATA__">{"preRenderData":{"url":"/"}}</script><script nomodule="">!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()},!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script><script crossorigin="anonymous" src="/bundle.62209.esm.js" type="module"></script><script nomodule="" src="/polyfills.f9ea8.js"></script><script nomodule="" defer="defer" src="/bundle.31a3a.js"></script></body></html>

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package.json
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{
"private": true,
"name": "appuntiweb",
"version": "0.3.12",
"version": "0.3.13",
"license": "AGPL-3.0-or-later",
"scripts": {
"start": "preact watch",

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src/routes/ApprendimentoSistemiArtificiali.js
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@ -468,7 +468,69 @@ export default function(props) {
<Section title={"Modello a percettroni"}>
<Panel title={"Cos'è?"}>
<p>
Un modello di rete neurale che supporta l'apprendimento e che presenta <b>più strati di neuroni</b> (detti <i>percettroni</i>).
Un modello di rete neurale che supporta l'apprendimento e che presenta <b>più strati di neuroni</b>.
</p>
</Panel>
<TablePanel>
<thead>
<tr>
<th>Simbolo</th>
<th>Descrizione</th>
</tr>
</thead>
<tbody>
<tr>
<td><BLatex>{r`N`}</BLatex></td>
<td>Numero totale di neuroni nel sistema</td>
</tr>
<tr>
<td><BLatex>{r`n`}</BLatex></td>
<td>Numero di un neurone specifico</td>
</tr>
<tr>
<td><BLatex>{r`w_{nm}`}</BLatex></td>
<td>Intensità della sinapsi diretta da <ILatex>{r`n`}</ILatex> verso <ILatex>{r`m`}</ILatex></td>
</tr>
<tr>
<td><BLatex>{r`x_n`}</BLatex></td>
<td>Emissione del neurone <ILatex>{r`n`}</ILatex></td>
</tr>
<tr>
<td><BLatex>{r`H(v)`}</BLatex></td>
<td>Funzione che restituisce lo stato di un neurone dato un valore di input</td>
</tr>
<tr>
<td><BLatex>{r`\sum_1^N ( w_n \cdot x_n )`}</BLatex></td>
<td>Somma degli input di un neurone</td>
</tr>
<tr>
<td><BLatex>{r`x_0`}</BLatex></td>
<td>Neurone di offset</td>
</tr>
</tbody>
</TablePanel>
</Section>
<Section>
<Panel title={"Percettrone"}>
<p>
<b>Neurone terminale</b> di una rete di percettroni.
</p>
<p>
Utilizza un <b>semipiano di attivazione</b> invece che una soglia.
</p>
<p>
Ai neuroni in input si aggiunge un neurone speciale <ILatex>{r`x_0`}</ILatex> detto <b>neurone di offset</b> che permette di semplificare la formula.
</p>
<p>
<Todo>TODO: scrivere e imparare le formule</Todo>
</p>
</Panel>
<Panel title={"Apprendimento"}>
<p>
Si parte da intensità casuali delle sinapsi.
</p>
<p>
Si prova a classificare degli esempi pre-classificati: se un esempio viene classificato nel modo sbagliato, si alterano le intensità delle sinapsi in direzione della sua classificazione corretta.
</p>
</Panel>
</Section>