{"id":3540,"date":"2025-11-24T11:12:57","date_gmt":"2025-11-24T10:12:57","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/design-a-neural-network\/"},"modified":"2026-02-11T10:27:01","modified_gmt":"2026-02-11T09:27:01","slug":"design-a-neural-network","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/design-a-neural-network\/","title":{"rendered":"Build a neural network in 7 steps"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3540\" class=\"elementor elementor-3540\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2bbeb2ef elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2bbeb2ef\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-36eae42c\" data-id=\"36eae42c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b6f1806 elementor-widget elementor-widget-text-editor\" data-id=\"b6f1806\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>In this tutorial, you will build a neural network that approximates a function defined by a set of data points. Download the data for this application from the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2024\/04\/data.csv\">data.csv<\/a>\u00a0file.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b15cf51 elementor-widget elementor-widget-text-editor\" data-id=\"b15cf51\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>To complete this example, follow these steps:<\/p><section><\/section><section><ol><li><a href=\"#CreateApproximationProject\">Create an approximation model<\/a>.<\/li><li><a href=\"#CponfigureDataSet\">Configure data set<\/a>.<\/li><li><a href=\"#SetNetworkArchitecture\">Set network architecture<\/a>.<\/li><li><a href=\"#TrainNeuralNetwork\">Train neural network<\/a>.<\/li><li><a href=\"#ImproveGeneralizationPerformance\">Improve generalization performance<\/a>.<\/li><li><a href=\"#TestResults\">Test results<\/a>.<\/li><li><a href=\"#DeployModel\">Deploy model<\/a>.<\/li><\/ol><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e03449c elementor-widget elementor-widget-text-editor\" data-id=\"e03449c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2 id=\"CreateApproximationProject\">1. Create approximation model<\/h2><p>Open Neural Designer. The start page is shown.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-start.webp\" width=\"80%\" \/><\/p><p data-start=\"770\" data-end=\"917\">Click\u00a0<em>New approximation model<\/em>. Then save the project file in the same folder as the data file. Neural Designer now displays the main workspace.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-dataset-blankt.webp\" width=\"80%\" \/><\/p><h2 id=\"ConfigureDataSet\">2. Configure data set<\/h2><p>After creating the model, go to the\u00a0<em>Data set<\/em>\u00a0page and click on the\u00a0<em>Browse data file<\/em>\u00a0button. A file selection dialog will appear. Select the file\u00a0<em data-start=\"1076\" data-end=\"1086\">data.csv<\/em>\u00a0and click\u00a0<em>Finish<\/em>. The program will automatically load the data and apply the default configuration.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-dataset-loaded2.webp\" width=\"80%\" \/><\/p><p>This data set has 2 variables and 50 samples.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-06d6754 elementor-widget elementor-widget-text-editor\" data-id=\"06d6754\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"1212\" data-end=\"1262\">Next, define how the model will use the variables:<\/p><ul data-start=\"1264\" data-end=\"1405\"><li data-start=\"1264\" data-end=\"1334\"><p data-start=\"1266\" data-end=\"1334\">The first column represents the independent\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputVariables\">(input) variable<\/a>,\u00a0<em data-start=\"1328\" data-end=\"1331\">x<\/em>.<\/p><\/li><li data-start=\"1335\" data-end=\"1405\"><p data-start=\"1337\" data-end=\"1405\">The second column represents the dependent\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TargetVariables\">(target) variable<\/a>,\u00a0<em data-start=\"1399\" data-end=\"1402\">y<\/em>.<\/p><\/li><\/ul><p data-start=\"1407\" data-end=\"1438\">Keep the default configuration.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fb6523 elementor-widget elementor-widget-text-editor\" data-id=\"3fb6523\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"1440\" data-end=\"1489\">Then define how the model will use the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Instances\">instances<\/a>:<\/p><ul data-start=\"1491\" data-end=\"1553\"><li data-start=\"1491\" data-end=\"1511\"><p data-start=\"1493\" data-end=\"1511\">60% for\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TrainingInstances\">training<\/a><\/p><\/li><li data-start=\"1512\" data-end=\"1533\"><p data-start=\"1514\" data-end=\"1533\">20% for\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#SelectionInstances\">selection<\/a><\/p><\/li><li data-start=\"1534\" data-end=\"1553\"><p data-start=\"1536\" data-end=\"1553\">20% for\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TestingInstances\">testing<\/a><\/p><\/li><\/ul><p data-start=\"1555\" data-end=\"1589\">Keep these default values as well.<\/p><p data-start=\"1591\" data-end=\"1802\">After configuring the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">dataset<\/a>\u00a0, you can explore it further. For example, open\u00a0<em>Task Manager &gt; Data set &gt; Plot scatter chart<\/em>. The Viewer window displays a chart of the data. The plot reveals a sinusoidal shape.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2024\/04\/Screenshot-scatter.png\" width=\"80%\" \/><\/p><p>You can also evaluate data quality by running additional dataset analysis tasks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c5468c elementor-widget elementor-widget-text-editor\" data-id=\"8c5468c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>\u00a0<\/p><h2 id=\"SetNetworkArchitecture\">3. Set network architecture<\/h2><p>Next, click on the\u00a0<em>Neural network<\/em>\u00a0tab to configure the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Approximation\">approximation<\/a>\u00a0model. The next screenshot shows this page.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-neural-network-options.webp\" width=\"80%\" \/><\/p><p data-start=\"1981\" data-end=\"2108\">Focus on the\u00a0<em>Perceptron layers<\/em>\u00a0section. By default, the network contains two layers: one hidden layer and one output layer.<\/p><ul data-start=\"2110\" data-end=\"2330\"><li data-start=\"2110\" data-end=\"2196\"><p data-start=\"2112\" data-end=\"2196\">The hidden layer includes 3 neurons with a<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#HyperbolicTangentActivationFunction\">\u00a0hyperbolic tangent activation function<\/a>.<\/p><\/li><li data-start=\"2197\" data-end=\"2274\"><p data-start=\"2199\" data-end=\"2274\">Since the model has one target (<em data-start=\"2231\" data-end=\"2234\">y<\/em>), the output layer includes 1 neuron.<\/p><\/li><li data-start=\"2275\" data-end=\"2330\"><p data-start=\"2277\" data-end=\"2330\">The output layer uses a<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#LinearActivationFunction\">\u00a0linear activation function<\/a>.<\/p><\/li><\/ul><p data-start=\"2332\" data-end=\"2360\">Keep these default settings.<\/p><p data-start=\"2362\" data-end=\"2526\">To visualize the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#NetworkArchitecture\">network architecture<\/a>, open\u00a0<em>Task manager &gt; Neural network &gt; Report neural network<\/em>. The Viewer window displays a graphical representation of the network.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2024\/04\/Screenshot-architecture.png\" width=\"80%\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-98c2010 elementor-widget elementor-widget-text-editor\" data-id=\"98c2010\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>4. Train neural network<\/h2><p data-start=\"2561\" data-end=\"2668\">Next, open the\u00a0<em>Training strategy\u00a0<\/em>page. Here you configure the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#ErrorTerm\">error method<\/a>\u00a0and\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#OptimizationAlgorithm\">optimization algorithm<\/a>.<\/p><p>\u00a0The next figure shows this page.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-neural-training-options.webp\" width=\"80%\" \/><\/p><p>The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#NormalizedSquaredError\">normalized squared error\u00a0<\/a>is the default error method. A\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#RegularizationTerm\">regularization term\u00a0<\/a>is also added here. The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#QuasiNewtonMethod\">quasi-Newton method\u00a0<\/a>is the default\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#OptimizationAlgorithm\">optimization algorithm<\/a>. We leave that default values.<\/p><p>The most important task is the so-called\u00a0<em>Perform training<\/em>, which appears in the\u00a0<em>Task manager\u2019s<\/em>\u00a0list of\u00a0<em>Training strategy<\/em>\u00a0tasks.<br \/>Running that task, the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#OptimizationAlgorithm\">optimization algorithm\u00a0<\/a>minimizes the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#LossIndex\">loss index<\/a>, i.e., makes the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a>\u00a0fit the data set.<br \/>The following figure shows the results from the\u00a0<em>Perform training<\/em>\u00a0task in the\u00a0<em>Viewer<\/em>\u00a0window.<\/p><p>The figure above shows the training results. It displays how both the training (blue) and selection (orange) errors decrease during the training process.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-13285d1 elementor-widget elementor-widget-text-editor\" data-id=\"13285d1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2 id=\"ImproveGeneralizationPerformance\">5. Improve generalization performance<\/h2><p>The model selection tab shows the options that allow configuring the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">model selection<\/a> process. The next image shows the content of this page.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Screenshot-model-selection.webp\" width=\"80%\"><\/p><p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#IncrementalOrder\">Growing neurons&nbsp;<\/a>and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#GrowingInputs\">growing inputs&nbsp;<\/a>are the default options for <em>Neurons selection<\/em> and <em>Inputs selection<\/em>, respectively.<br><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Since we will use the four inputs, <\/span><em style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Input selection<\/em><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\"> will not be needed. To perform the <\/span><em style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Neurons selection<\/em><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">, double click on <\/span><em style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Task manager&gt; Model selection&gt; Neurons Selection&nbsp;<\/em><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">. The figure below shows the <\/span><em style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Viewer<\/em><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\"> windows with the results of this task.<\/span><\/p><p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-large wp-image-14776\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-1024x499.png\" alt=\"\" width=\"800\" height=\"390\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-1024x499.png 1024w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-300x146.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-768x374.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-1536x748.png 1536w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1-600x292.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection1.png 1907w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/p><p><img decoding=\"async\" class=\"aligncenter size-large wp-image-14777\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-1024x496.png\" alt=\"\" width=\"800\" height=\"388\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-1024x496.png 1024w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-300x145.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-768x372.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-1536x744.png 1536w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2-600x291.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/02\/neuron-selection2.png 1904w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/p><p>As we can see, an optimal <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#NetworkArchitecture\">network architecture <\/a>has been defined after performing the <em>Neurons selection<\/em> task. This redefined <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a> is already trained.<\/p><h2 id=\"TestResults\">&nbsp;<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7994c63 elementor-widget elementor-widget-text-editor\" data-id=\"7994c63\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>6. Test results<\/h2><p>There are several tasks to test the model that has been previously trained. These tasks are grouped under <em>Testing analysis<\/em> in the <em>Task manager<\/em>.<\/p><p>To test this model, double click on <em>Task manager&gt; Testing analysis&gt;\u00a0<\/em><em style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Perform goodness-of-fit analysis<\/em><em>\u00a0<\/em>The next figure shows the results of this task.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2024\/04\/Screenshot-regression.png\" width=\"80%\" \/><\/p><p>As we can observe in the table shown in the <em>Viewer<\/em>, the correlation is very close to 1, so we can say the model predicts well.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4cd22cd3 elementor-widget elementor-widget-text-editor\" data-id=\"4cd22cd3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><h2 id=\"DeployModel\">7. Deploy model<\/h2><p>Once you have tested the model, it is ready to make predictions. The <em>Model deployment<\/em> menu from the <em>Task manager<\/em> contains the tasks for this purpose.<\/p><p>Double click on <em>Task manager&gt; Model deployment&gt; Plot directional output <\/em>to see the output variation as a function of a single input. The picture below shows the results after performing this task.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2024\/04\/Screenshot-directional-outputs.png\" width=\"80%\" \/><\/p><p><em>Model deployment<\/em> tasks also include the option of writing the expression of the predictive model, in addition to exporting this expression to Python or C. The Python code corresponding to this model is presented below.<\/p><pre>import numpy as np\n\ndef scaling_layer(inputs):\n\n\toutputs = [None] * 1\n\toutputs[0] = inputs[0]*0.3183101416+0\n\treturn outputs;\n\ndef perceptron_layer_0(inputs):\n\n\tcombinations = [None] * 3\n\tcombinations[0] = 0.0544993 +1.84365*inputs[0] \n\tcombinations[1] = -1.40552 -1.52111*inputs[0] \n\tcombinations[2] = 1.47629 -1.67521*inputs[0] \n\tactivations = [None] * 3\n\tactivations[0] = np.tanh(combinations[0])\n\tactivations[1] = np.tanh(combinations[1])\n\tactivations[2] = np.tanh(combinations[2])\n\treturn activations;\n\n\ndef perceptron_layer_1(inputs):\n\n\tcombinations = [None] * 1\n\tcombinations[0] = -0.094707 +1.89925*inputs[0] +1.47562*inputs[1] +1.54961*inputs[2] \n\tactivations = [None] * 1\n\tactivations[0] = combinations[0]\n\treturn activations;\n\n\ndef unscaling_layer(inputs):\n\n\toutputs = [None] * 1\n\toutputs[0] = inputs[0]*1.270824432+0.2996354103\n\treturn outputs\n\n\ndef bounding_layer(inputs):\n\n\toutputs = [None] * 1\n\toutputs[0] = inputs[0]\n\treturn outputs\n\n\ndef neural_network(inputs):\n\n\toutputs = [None] * len(inputs)\n\toutputs = scaling_layer(inputs)\n\toutputs = perceptron_layer_0(outputs)\n\toutputs = perceptron_layer_1(outputs)\n\toutputs = unscaling_layer(outputs)\n\toutputs = bounding_layer(outputs)\n\treturn outputs;\n<\/pre><p>To learn more, see the next example:<\/p><ul><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/iris-flowers-classification\">Classification of iris flowers from sepal and petal dimensions<\/a>.<\/li><\/ul><h2>Related post<\/h2><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":1556,"template":"","categories":[31],"tags":[36],"class_list":["post-3540","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-user-guide","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Build a neural network in 7 steps<\/title>\n<meta name=\"description\" content=\"Follow this step-by-step guide to build, train, test, and deploy a neural network that fits a data set. 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