Creative

                      // Fetch user data from the API endpoint
                      async function fetchUserData(userId) {
                        try {
                          const response = await fetch(`/api/users/${userId}`);
                          if (!response.ok) {
                            throw new Error('Network response was not ok');
                          }
                          const data = await response.json();
                          return data;
                        } catch (error) {
                          console.error('There has been a problem with your fetch operation:', error);
                        }
                      }
                    

                      
                      class User extends \Controller { 

                          public function list() 
                          {
                                  $users = Model::all();
                                  \Core\View::renderTemplate('users', ['users'=>$users],null);
                          }
                          
                      }
                      

                  // Docker Compose
                  version: '3.8' 
                  services: 
                    apache: 
                          build: 
                              context: ./apache 
                              dockerfile: Dockerfile 
                          ports: 
                            - "8080:80" 
                          volumes: 
                           - ./www:/var/www/html 
                          depends_on: 
                            - db 
                          networks:
                             - lamp-network 

                  

                      // Machine Learning MatLab
                      '%  Instructions
                      %  ------------
                      % 
                      %  This file contains code that helps you get started on the
                      %  linear exercise. You will need to complete the following functions 
                      %  in this exericse:
                      %
                      %     sigmoidGradient.m
                      %     randInitializeWeights.m
                      %     nnCostFunction.m
                      %
                      %'
                      %% Initialization
                      clear ; close all; clc
                      

                      %% Setup the parameters
                      input_layer_size  = 400;  % 20x20 Input Images of Digits
                      hidden_layer_size = 25;   % 25 hidden units
                      num_labels = 10;          % 10 labels, from 1 to 10   
                      

                      %% =========== Part 1: Loading and Visualizing Data =============

                      fprintf('Loading and Visualizing Data ...\n')
                      function g = sigmoid(z)
                      g = 1.0 ./ (1.0 + exp(-z));
                      end