asankulov

(function main() {
  const origArr = [
    'AMOR', 'XISELA', 'JAMON', 'ROMA', 'OMAR', 'MORA', 'ESPONJA', 
    'RAMO', 'JAPONES', 'ARMO', 'MOJAN', 'MARO', 'ORAM', 'MONJA', 'ALEXIS'
  ];
  groupSimilarWordsAndPrintThem(origArr);
  
  const str = 'foo(foo(bar(str))(opg))blim';
  removeParenthesesAndReverseTheWordsInsideThem(str);
}());

asankulov

class Course(models.Model):
    class Meta:
        verbose_name = 'Course'
        verbose_name_plural = 'Courses'

    trailer = models.FileField(verbose_name='Trailer', upload_to='course_trailers/')
    price = models.PositiveSmallIntegerField(verbose_name='Price')
    author = models.ForeignKey(CustomUser, on_delete=models.SET_NULL, null=True, blank=True, verbose_name='Author',
                               related_name='courses')
    title = models.CharField(max_length=255, verbose_name='Title')

asankulov

version: "3"
services:
  nuxt_app:
    container_name: nuxt_app
    restart: on-failure
    build: ./nuxt_app
    depends_on:
      - mongo
    command: ["npm", "start"]
    env_file:

asankulov

- name: DO
  hosts: localhost
  vars:
    project_name: "PUT A NAME FOR YOUR PROJECT HERE"
    do_token: "PUT YOUR DIGITAL OCEAN API KEY HERE ==> https://cloud.digitalocean.com/settings/api/tokens"
    repository: "PUT YOUR REPOSITORY URL HERE"

  tasks:
    - name: LOCAL | Generate SSH key
      shell: ssh-keygen -b 2048 -t rsa -f ~/.ssh/{{project_name}} -q -N ""

asankulov

class ExchangeAPITestCase(APITestCase):
    def setUp(self) -> None:
        self.valid_base_query_param = 'USD'
        self.valid_target_query_param = 'EUR'
        self.valid_amount_query_param = 12.12
        self.valid_convert_return_value = 1404.75
        self.valid_serializer_data = {
            'base': self.valid_base_query_param,
            'target': self.valid_target_query_param,
            'amount': self.valid_amount_query_param

asankulov

##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

asankulov

'''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

asankulov

var doctors = [
    { number: 1,  actor: "William Hartnell",      begin: 1963, end: 1966 },
    { number: 2,  actor: "Patrick Troughton",     begin: 1966, end: 1969 },
    { number: 3,  actor: "Jon Pertwee",           begin: 1970, end: 1974 },
    { number: 4,  actor: "Tom Baker",             begin: 1974, end: 1981 },
    { number: 5,  actor: "Peter Davison",         begin: 1982, end: 1984 },
    { number: 6,  actor: "Colin Baker",           begin: 1984, end: 1986 },
    { number: 7,  actor: "Sylvester McCoy",       begin: 1987, end: 1989 },
    { number: 8,  actor: "Paul McGann",           begin: 1996, end: 1996 },
    { number: 9,  actor: "Christopher Eccleston", begin: 2005, end: 2005 },

asankulov

FROM nginx:alpine AS builder

# nginx:alpine contains NGINX_VERSION environment variable, like so:
# ENV NGINX_VERSION 1.15.0

# Our NCHAN version
ENV NCHAN_VERSION 1.1.15

# Download sources
RUN wget "http://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz" -O nginx.tar.gz && \

asankulov

Prepare files & libraries

1. Download selenium standalone server from: http://goo.gl/rQhaxb, Create local folder at ~/selenium and copy or move jar to that path:

$ mkdir ~/selenium
$ cd ~/selenium
$ wget http://goo.gl/rQhaxb
$ mv selenium-server-standalone-2.49.1.jar ~/selenium/