hesoyamcode

git log --graph --decorate --oneline

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{
    "presets": [
        "env"
    ]
}

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Node:latest
docker exec -it container_name /bin/bash

node:alpine
docker exec -it container_name /bin/sh

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The "size" and "virtual size" describe the amount of disk space used by a container. Let me try and explain:

When starting a container, the image that the container is started from is mounted read-only. On top of that, a writable layer is mounted, in which any changes made to the container are written.

The read-only layers of an image can be shared between any container that is started from the same image, whereas the "writable" layer is unique per container (because: you don't want changes made in container "a" to appear in container "b" 😄)

Back to the docker ps -s output;

- The "size" information shows the amount of data (on disk) that is used for the writable layer of each container
- The "virtual size" is the amount of disk-space used for the read-only image data used by the container.

hesoyamcode

from gensim.models.word2vec import Word2Vec
from sklearn.manifold import TSNE
from sklearn.datasets import fetch_20newsgroups
import re
import matplotlib.pyplot as plt

# download example data ( may take a while)
train = fetch_20newsgroups()

def clean(text):

hesoyamcode

cd into directory you want to remove .DS_Store

then type 

find . -name '.DS_Store' -type f -delete

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import React, { Component } from 'react';
import PropTypes from 'prop-types';

class ProgressiveImage extends Component {
  static propTypes = {
    preview: PropTypes.string,
    image: PropTypes.string
  };

  state = {

hesoyamcode

Actually - there is no a good answer to your question. Most of the architectures are usually carefully designed and finetuned during many experiments. 
I could share with you some of the rules of thumbs one should apply when designing its own architecture:

Avoid a dimension collapse in the first layer. Let's assume that your input filter has a (n, n) spatial shape for RGB image.
In this case, it is a good practice to set the filter numbers to be greater than n * n * 3 as this is the dimensionality of the input of a single filter.
If you set smaller number - you could suffer from the fact that many useful pieces of information about the image are lost due to initialization which dropped informative dimensions.
Of course - this is not a general rule - e.g. for a texture recognition, where image complexity is lower - a small number of filters might actually help.

Think more about volume than filters number - when setting the number of filters it's important to think about the volume change instead of the chang

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*
 * Migration
 */
'use strict';
module.exports = {
  up: function(queryInterface, Sequelize) {
    return queryInterface.createTable('Users', {
      firstName: {
        type: Sequelize.STRING
      },

hesoyamcode

CREATE TABLE `apps_countries` (
`id` int(11) NOT NULL auto_increment,
`country_code` varchar(2) NOT NULL default '',
`country_name` varchar(100) NOT NULL default '',
PRIMARY KEY (`id`)
) ENGINE=MyISAM DEFAULT CHARSET=utf8;
-- 
-- Dumping data for table `apps_countries`
-- 
INSERT INTO `apps_countries` VALUES (null, 'AF', 'Afghanistan');