Antes de venir al taller, recomiendo tener las siguientes herramientas instaladas en su computadora:
- Terminal
- Git
- Node
- NPM
| import { createSlice, createAsyncThunk, PayloadAction } from '@reduxjs/toolkit'; | |
| import * as Sentry from '@sentry/electron/renderer'; | |
| import { getIpcApi } from '../lib/get-ipc-api'; | |
| import { DEFAULT_PHP_VERSION } from '../../vendor/wp-now/src/constants'; | |
| // ---- Types ---- | |
| interface WPCliItem { | |
| name: string; | |
| } |
| // Modify the destination object and copy paste this script in the chrome dev tools. | |
| // Very useful to track performance for API requests. | |
| function sleep(ms) { | |
| return new Promise((resolve) => setTimeout(resolve, ms)); | |
| } | |
| async function performanceURL(url, sampleSize, sleepMs = 1000) { | |
| const performanceList = []; | |
| for (let i = 0; i < sampleSize; i++) { | |
| const t0 = performance.now(); |
| // Navigation | |
| Tron.onCustomCommand({ | |
| title: 'Choose your NAVIGATION', | |
| description: 'Insert the screenName where you want to Navigate to.', | |
| command: 'navigateTo', | |
| args: [ | |
| { | |
| name: 'screenName', | |
| type: 'string', | |
| }, |
| sudo rm -rf LCD-show | |
| git clone https://github.com/goodtft/LCD-show.git | |
| chmod -R 755 LCD-show | |
| cd LCD-show/ | |
| sudo ./LCD35-show |
| {0: 'tench, Tinca tinca', | |
| 1: 'goldfish, Carassius auratus', | |
| 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
| 3: 'tiger shark, Galeocerdo cuvieri', | |
| 4: 'hammerhead, hammerhead shark', | |
| 5: 'electric ray, crampfish, numbfish, torpedo', | |
| 6: 'stingray', | |
| 7: 'cock', | |
| 8: 'hen', | |
| 9: 'ostrich, Struthio camelus', |
| """ | |
| This script will demonstrate how to use a pretrained model, in PyTorch, | |
| to make predictions. Specifically, we will be using VGG16 with a cat | |
| image. | |
| References used to make this script: | |
| PyTorch pretrained models doc: | |
| http://pytorch.org/docs/master/torchvision/models.html | |
| PyTorch image transforms example: | |
| http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#transforms |
| import * as React from "react" | |
| import { observer } from "mobx-react" | |
| import {Register} from "../register" | |
| import {Home} from "../home" | |
| import { MSTUser } from "../../app/models/user" | |
| import { NavigationScreenProps } from "react-navigation" | |
| export interface PreLoadingScreenProps extends NavigationScreenProps<{}> {} |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta http-equiv="X-UA-Compatible" content="IE=edge"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Bootstrap Boilerplate</title> | |
| <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css"> | |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"> | |
| </head> |
| 'use strict' | |
| // 0.- Nuestro primer componente | |
| class Hola extends React.Component { | |
| // 5.- A帽adimos la propiedad state | |
| state = { | |
| emoticono: '馃槑' | |
| } | |
| // 6.- M茅todo donde utilizamos la funcion `setState` | |
| cambiarEmoticono = () => { | |
| let emoticono = '馃ぉ' |