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Mohamed3on / batchPrettier.md
Last active April 5, 2024 17:03
Run prettier on all JS files in a directory
  1. Install prettier
  2. Make a .prettierignore file, and add directories you'd like prettier to not format, for example: **/node_modules
  3. Run prettier --write "**/*.js" *Don't forget the quotes.
  4. Optional: if you want to format JSON/SCSS files too, replace js with json/scss.
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Mohamed3on / CordovaDevtools.md
Last active June 8, 2023 07:45
Inspect Cordova/Ionic apps with Chrome Devtools
  1. Connect your Android device
  2. Make sure it's working correctly with adb and you can find it when you run adb devices
  3. In the directory of your app, run cordova run android --device, Now the app will run on your device
  4. Go to chrome://inspect/#devices on your Chrome browser, and select your app and click inspect
  5. Now you can inspect the HTML/CSS, and debug your JS with ease!
@Mohamed3on
Mohamed3on / cloudSettings
Last active October 2, 2021 06:25
Visual Studio Code Settings Sync Gist
{"lastUpload":"2021-10-02T06:25:20.555Z","extensionVersion":"v3.4.3"}
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Mohamed3on / The Technical Interview Cheat Sheet.md
Last active April 23, 2020 20:47 — forked from tsiege/The Technical Interview Cheat Sheet.md
This is a fork to fix the markdown errors in the original gist.

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

Array

Definition:

  • Stores data elements based on an sequential, most commonly 0 based, index.
  • Based on tuples from set theory.
// Future versions of Hyper may add additional config options,
// which will not automatically be merged into this file.
// See https://hyper.is#cfg for all currently supported options.
module.exports = {
config: {
// default font size in pixels for all tabs
fontSize: 14,
// font family with optional fallbacks
function [J grad] = nnCostFunction(nn_params, ...
input_layer_size, ...
hidden_layer_size, ...
num_labels, ...
X, y, lambda)
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
% [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
% X, y, lambda) computes the cost and gradient of the neural network. The
% parameters for the neural network are "unrolled" into the vector