{"id":11881,"date":"2023-12-14T08:58:22","date_gmt":"2023-12-14T07:58:22","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/?post_type=blog&#038;p=11881"},"modified":"2025-09-22T18:52:28","modified_gmt":"2025-09-22T16:52:28","slug":"use-of-the-neuralengine-using-ndt-xml-files","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/use-of-the-neuralengine-using-ndt-xml-files\/","title":{"rendered":"Integrate Neural Designer in your application"},"content":{"rendered":"<p data-start=\"144\" data-end=\"539\">Integrating Neural Designer into your application can simplify the adoption of machine learning within your business.<\/p>\n<p data-start=\"199\" data-end=\"310\">Companies often find it challenging to integrate machine learning into their platforms.<\/p>\n<p data-start=\"199\" data-end=\"310\">A practical solution is to use software that runs without installation.<\/p>\n<p data-start=\"144\" data-end=\"539\">Neural Designer offers this option, and this post explains how to use it to apply machine learning in your projects quickly.<\/p>\n<h2>Contents:<\/h2>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li><a href=\"#Introduction\">Introduction<\/a><\/li>\n<li><a href=\"#Tasks\">Tasks<\/a>\n<ol>\n<li><a href=\"#Performtraining\">Perform training<\/a><\/li>\n<li><a href=\"#Randomizeparameters\">Randomize parameters<\/a><\/li>\n<li><a href=\"#Calculateoutputs\">Calculate outputs<\/a><\/li>\n<li><a href=\"#Performresponseoptimization\">Perform response optimization<\/a><\/li>\n<li><a href=\"#Exportwriteexpression\">Export write expression<\/a><\/li>\n<li><a href=\"#Exportneuralnetworkstoprogramminglanguage\">Export neural networks to a programming language<\/a>\n<ol>\n<li><a href=\"#ExporttoPHP\">Export to PHP<\/a><\/li>\n<li><a href=\"#ExporttoPython\">Export to Python<\/a><\/li>\n<li><a href=\"#ExporttoC\">Export to C<\/a><\/li>\n<li><a href=\"#ExporttoJavaScript\">Export to JavaScript<\/a><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/li>\n<li><a href=\"#HowtoexecuteNeuralEngine\">How to execute NeuralEngine<\/a>\n<ol>\n<li><a href=\"#Ubuntu\">Ubuntu<\/a>\n<ol>\n<li><a href=\"#Warning\">Warning<\/a><\/li>\n<\/ol>\n<\/li>\n<li><a href=\"#Windows\">Windows<\/a><\/li>\n<\/ol>\n<\/li>\n<li><a href=\"#Outputfile\">Output file<\/a><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<section id=\"Introduction\">\n<h2>Introduction<\/h2>\n<p>To understand this tutorial, it&#8217;s important to know the terms explained below.<\/p>\n<p>Integrate Neural Designer in your application by learning how its components work together:<\/p>\n<ul>\n<li>&#8220;<em>Neural Designer<\/em>&#8221; is the graphical environment we use to handle the model.<\/li>\n<li>&#8220;<em>NeuralEngine<\/em>&#8221; is the executable we call using &#8220;.ndt&#8221; files to carry out tasks.<\/li>\n<li>&#8220;<em>NeuralViewer<\/em>&#8221; is responsible for displaying the results collected in the &#8220;.ndo&#8221; file.<\/li>\n<li>The &#8220;.ndt&#8221; or &#8220;taskfiles&#8221;, which are also XML files that send a work order to the &#8220;neuralengine,&#8221; will have communication between these components through XML files, as emphasized in this post.<\/li>\n<\/ul>\n<p>The following section explores some tasks we can use in the NeuralEngine.<\/p>\n<p>You can learn more in the post <em data-start=\"31\" data-end=\"59\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/neural-designer-components\/\">Neural Designer components<\/a><\/em>, which explains the three main parts of the application and how they interact.<\/p>\n<\/section>\n<section id=\"Tasks\">\n<h2>Tasks<\/h2>\n<article class=\"text-token-text-primary w-full focus:outline-none scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-68c14edc-00d8-832b-af79-36da58cd2575-6\" data-testid=\"conversation-turn-402\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] thread-sm:[--thread-content-margin:--spacing(6)] thread-lg:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] thread-lg:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"03b3221f-ce2b-40cb-aff4-6e56a6b9573c\" data-message-model-slug=\"gpt-5\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose dark:prose-invert w-full break-words light markdown-new-styling\">\n<p data-start=\"0\" data-end=\"140\" data-is-last-node=\"\" data-is-only-node=\"\">As mentioned above, the\u00a0<span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\"><em>neural engine<\/em> creates a task file<\/span> for each action, defining the order and following the given rules or restrictions.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<p>Knowing that, let&#8217;s analyze some of the most critical tasks.<\/p>\n<\/section>\n<section id=\"Performtraining\">\n<h3>Perform training:<\/h3>\n<p>This task merely signals the need for training the model without altering the current specifications.<\/p>\n<p>The project file &#8220;.ndp&#8221; contains a dedicated section that defines training specifications; however, for this scenario, it&#8217;s inconsequential since the task doesn&#8217;t modify the established settings.<\/p>\n<\/section>\n<section id=\"Randomizeparameters\">\n<h3>Randomize parameters:<\/h3>\n<p>Next, we have this other task that sets random values to all the variables of the neural network.<\/p>\n<p>In this way, we will lose the adjustment made to the model by previous training, so the next training will have to start from 0.<\/p>\n<p>Therefore, we should not modify this task either.<\/p>\n<\/section>\n<section id=\"Calculateoutputs\">\n<h3>Calculate outputs:<\/h3>\n<p>Thanks to this task, the neural network will calculate its output based on the inputs we send it.<\/p>\n<p>Therefore, we must modify this task to define the desired input values for each variable.<\/p>\n<p>According to the company&#8217;s standard, the input values are defined twice, first in the &lt;Inputs&gt; tag and then in the &lt;InputsNames&gt; tag.<\/p>\n<p>Here&#8217;s an example extracted from the demonstration provided in this article:<\/p>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n   &lt;NeuralNetworkTask&gt;Calculate outputs\r\n      &lt;Inputs&gt;265.444;86.2852;62.7953;183.059;6.99577;956.059;764.377\r\n      &lt;\/Inputs&gt;\r\n      &lt;InputsNames&gt;265.444;86.2852;62.7953;183.059;6.99577;956.059;764.377\r\n      &lt;\/InputsNames&gt;\r\n   &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;<\/pre>\n<\/section>\n<section id=\"Performresponseoptimization\">\n<h3>Perform response optimization:<\/h3>\n<p>Using this last task, the &#8220;neuralengine&#8221; will find the optimal values for the variables of a process for some given conditions\/restrictions.<\/p>\n<p>We can define these restrictions ourselves in the TaskFile using the symbols that will be specified below:<\/p>\n<ul>\n<li>Used Token:: <strong>None<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">This variable will not affect the process of the response optimization task.<\/p>\n<ul>\n<li>Used Token:: <strong>&amp;lt;<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">&#8220;Less than&#8221;. Only the rows with a value less than the value defined for this variable are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>&amp;lt;=<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">&#8220;Less than or equal&#8221;. Only rows with a value less than or equal to the value defined for this variable are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>&gt;<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">&#8220;Greater than&#8221;. Only rows with a value greater than the value defined for this variable are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>&gt;=<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">&#8220;Greater than or equal&#8221;. Only rows with a value Greater than or equal to the value defined for this variable are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>=<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">&#8220;Equal to&#8221;. Only rows with a value equal to the value defined for this variable are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>Between<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">A range will be established, and only rows with a value between the minimum and maximum of that range are considered.<\/p>\n<ul>\n<li>Used Token:: <strong>Maximize<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">This variable&#8217;s value will be maximized with the restrictions previously mentioned.<\/p>\n<ul>\n<li>Used Token:: <strong>Minimize<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;\">This variable&#8217;s value will be minimized with the restrictions previously mentioned.<\/p>\n<p>By the standard used in the company, the instructions for each variable are defined in the &lt;Conditions&gt; tag, and then, in the &lt;Values&gt; tag, the values for each condition are defined.<\/p>\n<p>Here&#8217;s an example extracted from the demonstration provided in this article:<\/p>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n\u00a0\r\n   &lt;NeuralNetworkTask&gt;Perform response optimization\r\n\u00a0 \u00a0 \u00a0 &lt;Conditions&gt;Between None &amp;lt; &gt; None &gt; None Minimize&lt;\/Conditions&gt;\r\n      &lt;Values&gt;102 540 0 0 0 0 800 0 0&lt;\/Values&gt;\r\n   &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;\r\n<\/pre>\n<section id=\"Exportwriteexpression\">\n<h3>Export write expression<\/h3>\n<p>Thanks to this task, we can generate the mathematical expression corresponding to the current state of the neural network used in the model, i.e., the list of mathematical calculations the neural network makes to calculate the output based on the input.<\/p>\n<p>You should not modify this task.<\/p>\n<\/section>\n<section id=\"Exportneuralnetworktoprogramminglanguage\">\n<h3>Export the neural network to a programming language<\/h3>\n<p>Using this &#8220;task,&#8221; we can export the mathematical expression corresponding to the neural network translated to different programming languages, among which we can find C, PHP, JavaScript, and Python.<\/p>\n<p>The files for C and Python comprise straightforward scripts that compute the output using the neural network.<\/p>\n<p>However, the JavaScript and PHP files differ slightly.<\/p>\n<p>The JavaScript code is designed to create a table in HTML, visually modifying the network inputs.<\/p>\n<p>Conversely, the PHP code aims to be integrated as an API. This Task changes depending on the type of file generated.<\/p>\n<\/section>\n<section>\n<h4>Export to PHP<\/h4>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n   &lt;NeuralNetworkTask&gt;Export to PHP\r\n      &lt;PHPFileName&gt;\/Your\/directory\/model.php\r\n      &lt;\/PHPFileName&gt;\r\n   &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;<\/pre>\n<\/section>\n<section>\n<h4>Export to Python<\/h4>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n   &lt;NeuralNetworkTask&gt;Export to Python\r\n      &lt;PythonFileName&gt;\/Your\/directory\/model.py\r\n      &lt;\/PythonFileName&gt;\r\n   &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;<\/pre>\n<\/section>\n<section>\n<h4>Export to C<\/h4>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n   &lt;NeuralNetworkTask&gt;Export to C\r\n      &lt;CfileName&gt;\/Your\/directory\/model.c\r\n      &lt;\/CFileName&gt;\r\n    &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;<\/pre>\n<h4>Export to JavaScript<\/h4>\n<pre>&lt;NeuralDesignerTasks&gt;\r\n   &lt;NeuralNetworkTask&gt;Export to JavaScript\r\n       &lt;JavaScriptFileName&gt;\/Your\/directory\/\/model.html\r\n       &lt;\/JavaScriptFileName&gt;\r\n    &lt;\/NeuralNetworkTask&gt;\r\n&lt;\/NeuralDesignerTasks&gt;<\/pre>\n<\/section>\n<section id=\"HowtoexecuteNeuralEngine\">\n<h2>How to execute NeuralEngine<\/h2>\n<p>Now that we have defined and explained the &#8220;Tasks&#8221;, let&#8217;s move on to the section where we will explain the instructions to execute the &#8220;NeuralEngine&#8221; depending on the operating system.<\/p>\n<\/section>\n<section id=\"Ubuntu\">\n<h3>Ubuntu<\/h3>\n<p>First, a folder stores the project files, tasks, and necessary libraries to avoid dependency problems.<\/p>\n<p>The folder consists of three subdirectories and one document, and for this specific example, we will call it<strong> Linux<\/strong>:<\/p>\n<ul>\n<li><strong>neuralengine<\/strong>: In this folder, the executable and the libraries that avoid dependency problems are stored.<\/li>\n<li><strong>project<\/strong>: Here is stored both the project file &#8220;.ndp&#8221; and all other related files: &#8220;.csv&#8221;, &#8220;ndo&#8221;,&#8230;<\/li>\n<li><strong>tasks<\/strong>: Finally, this directory will host the different tasks that will later be used to give different execution commands to the engine.<\/li>\n<li><strong>run_neuralengine.sh: <\/strong>This bash\/shell script will launch the neuralengine when executed. (It must have run permissions to be launched).<\/li>\n<\/ul>\n<p>The folder will look like this:<br \/>\n<img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/Folder-Linux-1024x714.jpg\" alt=\"\" width=\"456\" height=\"318\" \/><\/p>\n<p>To make things simpler, we have created a small demo, and you can download it at the following link:<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/linux.zip\">Linux_demo<\/a><\/p>\n<p>You can modify the project folder to work on the desired project while modifying the task folder to add the desired tasks.<\/p>\n<p>Still, <strong><u>to avoid execution problems, you should not modify the NeuralEngine directory.<\/u><\/strong><\/p>\n<p>Now that the working directory is ready, follow the instructions below to get a correct execution:<\/p>\n<ul>\n<li>First, copy the path of the Linux folder provided for this demo, for example:<\/li>\n<\/ul>\n<p><strong>&#8220;\/home\/Artelnics\/demo&#8221;.<\/strong><\/p>\n<ul>\n<li>Then execute this command using the command line terminal:<\/li>\n<\/ul>\n<p><strong>Artelnics@artelnics:\/home$ cd \/home\/Artelnics\/demo\/linux.<\/strong><\/p>\n<p>(You can also open the command line terminal inside the &#8220;linux&#8221; directory to bypass this initial step.)<\/p>\n<ul>\n<li>As mentioned earlier, we require specific libraries to run the &#8220;neuralengine&#8221;; to avoid this issue, we have created the script &#8220;run_neuralengine.sh&#8221;. Which executes the file without dependency problems.<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\">If you open &#8220;run_neuralengine.sh&#8221; with a text editor, you will find 4 lines with four different commands. Next, remove the &#8220;#&#8221; symbol to uncomment the command you wish to execute upon launching &#8220;run_neuralengine.sh&#8221;:<\/p>\n<p style=\"padding-left: 40px;\"><strong>#$N_DIR\/neuralengine project\/domo.ndp tasks\/training_task.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>#$N_DIR\/neuralengine project\/domo.ndp tasks\/randomize_parameters.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>#$N_DIR\/neuralengine project\/domo.ndp tasks\/calculate_outputs_task.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>#$N_DIR\/neuralengine project\/domo.ndp tasks\/perform_response_optimization.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\">The command&#8217;s syntax is &#8220;Path\/to\/neuralengine Path\/to\/ProjectFile Path\/to\/TaskFile 1&#8221;.<\/p>\n<p style=\"padding-left: 40px;\">With this, we ask the engine to execute the task specified in the TaskFile to get the desired output\u00a0 (TaskFiles will be explained in the next section).<\/p>\n<p style=\"padding-left: 40px;\">Only these tasks are mentioned in this example, but all the tasks specified in the previous section can be used.<\/p>\n<ul>\n<li>Once we have uncommented the desired command, we will execute the following line \u00a0 using the previously opened command line terminal:<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong>Artelnics@artelnics:~demo\/linux$ sudo run_neuralengine.sh<\/strong><\/p>\n<\/section>\n<section id=\"Warning\">\n<h4>Warning<\/h4>\n<p>If during the execution of &#8220;run_neuralengine.sh&#8221; an error similar to &#8220;segmentation fault (core dumped)&#8221; occurs, try deleting the files inside the folder &#8220;neuralengine\/x86_64-linux-gnu\/&#8221; and relaunch the program.<\/p>\n<\/section>\n<section id=\"Windows\">\n<h2>Windows<\/h2>\n<p>First, a folder stores the project files, tasks, and necessary libraries to avoid dependency problems. The folder consists of three subdirectories and one document, and for this specific example, we will call it <strong>win64<\/strong>:<\/p>\n<ul>\n<li><strong>neuralengine<\/strong>: This folder stores the executable and libraries designed to circumvent dependency issues.<\/li>\n<li><strong>project<\/strong>: This location houses both the project file &#8220;.ndp&#8221; and all other associated files such as &#8220;.csv&#8221;, &#8220;ndo&#8221;,&#8230;<\/li>\n<li><b>tasks: <\/b>Ultimately, this directory serves as the repository for various tasks, which will later be utilized to provide diverse execution commands to the engine.<\/li>\n<\/ul>\n<p>The folder will look like this:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/Folder-Windows-1-1024x714.png\" alt=\"\" width=\"454\" height=\"316\" \/><\/p>\n<p>To make things simpler, we have created a small demo, and you can download it at the following link:<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/win64.zip\" target=\"_blank\" rel=\"noopener\">Demo_win64<\/a><\/p>\n<p>You can modify the project folder to work on the desired project while modifying the task folder to add the desired tasks.<\/p>\n<p>Still, <strong><u>to avoid execution problems, you should not modify the NeuralEngine directory.<\/u><\/strong><\/p>\n<p>Now that the working directory is ready, follow the instructions below to get a correct execution:<\/p>\n<ul>\n<li>First, copy the path of the win64 folder provided for this demo; for example:<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\"><strong>&#8220;<\/strong><strong>C<\/strong><strong>:<\/strong><strong>sersersersArtelnicsDesktopwin64&#8243;.<\/strong><\/p>\n<ul>\n<li>Then execute this command using the command line terminal:<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\"><strong>C:Yourdirectory&gt; cd C:UsersArtelnicsDesktopwin64&gt;.<\/strong><\/p>\n<ul>\n<li>Now, using the same command line, we can use the following commands to execute different tasks:<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\"><strong>C:UsersArtelnicsDesktopwin64&gt;neuralengineneuralengine.exe project\/domo.ndp tasks\/training_task.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>C:UsersArtelnicsDesktopwin64&gt;neuralengineneuralengine.exe project\/domo.ndp tasks\/randomize_parameters.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>C:UsersArtelnicsDesktopwin64&gt;neuralengineneuralengine.exe project\/domo.ndp tasks\/calculate_outputs_task.ndt 1<\/strong><\/p>\n<p style=\"padding-left: 40px;\"><strong>C:UsersArtelnicsDesktopwin64&gt;neuralengineneuralengine.exe project\/domo.ndp tasks\/perform_response_optimization.ndt 1<\/strong><\/p>\n<p>Following the instructions given for Ubuntu, the command syntax used on Windows is &#8220;Path\/to\/neuralengine Path\/to\/ProjectFile Path\/to\/TaskFile 1&#8221;.<\/p>\n<p>With this, we ask the engine to execute the task specified in the TaskFile to modify the ProjectFile (TaskFiles will be explained in the next section).<\/p>\n<p>Only these tasks are mentioned in this example, but all the tasks specified in the previous section can be used.<\/p>\n<\/section>\n<section id=\"Outputfile\">\n<h2>Output file:<\/h2>\n<p>The project file, &#8220;.ndp,&#8221; links to this file, which contains the output generated by each task.<\/p>\n<p>Each time a task runs, it stores the output displayed in the command line terminal into the &#8220;.ndo&#8221; file, also known as the output file.<\/p>\n<p>If, for any reason, all the generated outputs should be discarded and the text that has been accumulated in the &#8220;.ndo&#8221; file should be deleted, the &lt;NeuralDesignerOutput&gt; and &lt;\/NeuralDesignerOutput&gt; tags must be kept to maintain the correct functioning of the neuralengine.<\/p>\n<p><img decoding=\"async\" style=\"margin-right: 10px;\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/neuralengine.png\" alt=\"Primera imagen\" width=\"367\" height=\"213\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" style=\"margin-left: 10px;\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/12\/neuralengine1.png\" alt=\"Segunda imagen\" width=\"390\" height=\"152\" \/><\/p>\n<\/section>\n<\/section>\n<h2>Related Posts<\/h2>\n","protected":false},"author":1267,"featured_media":1556,"template":"","categories":[30],"tags":[36],"class_list":["post-11881","blog","type-blog","status-publish","has-post-thumbnail","hentry","category-tutorials","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Integrate Neural Designer in your application<\/title>\n<meta name=\"description\" content=\"Learn how to use the machine learning Neural Designer tool without any 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