nootanghimire

/******************************************************
 * @author Nootan Ghimire <nootan.ghimire@gmail.com>
 * @file   postfix-eval.cpp
 * @desc   Evaluation of Multi-Digit Postfix Expression
 *****************************************************/ 

// C++ Includes
#include <iostream>
#include <cctype>
#include <cstdlib>

dwayne

Chapter 1 - From zero to deploy

http://railstutorial.org/, http://railstutorial.org/book

Based on Rails 4.0, missing:

• Turbolinks
• Russian doll caching
• New RSpec abilities such as feature specs

kengoudsward

[assumes everything is installed]
cd ruby_rails  [ie, wherever you want to keep all your rails projects]
rails new_project  [create the project]
ruby new_project/script/server  [wow - your new app is up!]
[ctrl-c to kill it]

[in new_project dir, do]
git init
git add .
git status

diegodurs

Generate new app

Don't forget -T -d postgresql. One day create a template and builder

• rails new AppName -T -d postgresql

Use rspec, haml, coffee, scss

• rails generate rspec:install In application.rb:

techniq

declare module '@vx/axis' {
  import React from 'react';
  import { ScaleTime } from 'd3-scale';

  interface Point {
    x: number;
    y: number;
  }

  interface AxisProps {

guillaumegarcia13

HTML Forms

In order that you never go to W3Schools (never go there), here is a basic cheat sheet for writing simple HTML forms.

This is culled from a few sources, [the most important being MDN][MDN]. MDN (the Mozilla Developer Network) should be seen as "the docs" when you are having an issue with HTML.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

Ciantic

import { createConnection, getConnection, Entity, getRepository } from "typeorm";
import { PrimaryGeneratedColumn, Column } from "typeorm";

@Entity()
export class MyEntity {
    @PrimaryGeneratedColumn()
    id?: number;

    @Column()
    name?: string;

toboqus

#include <iostream>

using namespace std;


struct node{
	int value;
	node *left;
	node *right;
};

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman