{"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":"2026-08-27T21:27:43","modified_gmt":"2026-08-27T19:27:43","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":"<style>.ndg{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:20px 24px 10px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndg *{box-sizing:border-box}.ndg a{text-decoration:none}.ndg-wrap{width:min(100%,1000px);margin:0 auto}.ndg-lead{font-size:19px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 32px}.ndg-lead a{color:#2d799f;font-weight:600}.ndg-download-row{display:flex;justify-content:center;margin:14px 0 24px!important}.ndg-download{display:inline-flex;align-items:center;justify-content:center;padding:9px 15px;border-radius:9px;background:#2d799f;color:#fff!important;font-size:14px;font-weight:600;line-height:1.2;box-shadow:0 6px 14px rgba(30,83,116,.18);transition:background .2s ease,transform .2s ease}.ndg-download:hover{background:#245e80;color:#fff!important;transform:translateY(-1px)}.ndg-download:focus-visible{outline:3px solid rgba(86,161,200,.45);outline-offset:3px}.ndg-toc{list-style:none;counter-reset:s;display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:12px;margin:0 0 48px;padding:0}.ndg-toc li{counter-increment:s}.ndg-toc a{display:flex;align-items:center;gap:14px;padding:14px 18px;background:#f2f2f2;border-radius:12px;color:#12354b!important;font-size:15px;font-weight:600;box-shadow:-8px -8px 16px rgba(255,255,255,.9),8px 8px 16px rgba(30,83,116,.10)}.ndg-toc a:hover{color:#2d799f!important}.ndg-toc a:before{content:counter(s);display:flex;align-items:center;justify-content:center;width:28px;height:28px;flex:0 0 28px;border-radius:50%;background:#56a1c8;color:#fff;font-size:14px;font-weight:800}.ndg-step{display:grid;grid-template-columns:64px 1fr;gap:28px;margin:0 0 40px;padding:36px 38px;background:#f2f2f2;border-radius:22px;box-shadow:-14px -14px 28px rgba(255,255,255,.92),14px 14px 28px rgba(30,83,116,.12)}.ndg-step__no{display:flex;align-items:center;justify-content:center;width:64px;height:64px;border-radius:50%;background:linear-gradient(135deg,#56a1c8 0%,#245e80 100%);color:#fff;font-size:26px;font-weight:800}.ndg-step__body{min-width:0}.ndg-step__body h2{margin:8px 0 18px;color:#001233;font-size:26px;font-weight:700;line-height:1.2}.ndg-step__body h3{margin:26px 0 12px;color:#12354b;font-size:19px;font-weight:600}.ndg-step__body p{margin:0 0 16px;font-size:17px;line-height:1.62;color:#33424f}.ndg-step__body a{color:#2d799f;font-weight:500}.ndg-step__body ul,.ndg-step__body ol{margin:0 0 16px;padding-left:24px}.ndg-step__body li{margin:6px 0;font-size:17px;line-height:1.55;color:#33424f}.ndg-step__body img{display:block;width:auto!important;max-width:min(620px,100%);height:auto;margin:24px auto;border-radius:14px;box-shadow:0 14px 32px rgba(0,18,51,.14)}.ndg-step__body pre{margin:22px 0 6px;padding:22px 24px;background:#0b1830;color:#e6eef5;border-radius:14px;overflow-x:auto;font-family:Consolas,Menlo,\"Liberation Mono\",monospace;font-size:13.5px;line-height:1.6}@media(max-width:820px){.ndg-toc{grid-template-columns:1fr}.ndg-step{grid-template-columns:1fr;gap:16px;padding:26px 22px}.ndg-step__no{width:52px;height:52px;font-size:22px}.ndg-step__body h2{font-size:22px}}@media(max-width:640px){.ndg{padding:10px 14px}}<\/style><div class=\"ndg\"><div class=\"ndg-wrap\"><div class=\"ndg-lead\"><p>In this tutorial, you will build a neural network that learns a simple nonlinear relationship from a small CSV file. The example contains one input, <em>x<\/em>, and one target, <em>y<\/em>, related by <em>y = x\u00b2<\/em>.<\/p>\n<p class=\"ndg-download-row\"><a class=\"ndg-download\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/simple-approximation-dataset.csv\" download>Download example dataset<\/a><\/p>\n<p>To complete this example, follow these steps:<\/p>\n<\/div><ol class=\"ndg-toc\"><li><a href=\"#ndg-step-1\">Create approximation model<\/a><\/li><li><a href=\"#ndg-step-2\">Configure data set<\/a><\/li><li><a href=\"#ndg-step-3\">Set network architecture<\/a><\/li><li><a href=\"#ndg-step-4\">Train neural network<\/a><\/li><li><a href=\"#ndg-step-5\">Improve generalization performance<\/a><\/li><li><a href=\"#ndg-step-6\">Test results<\/a><\/li><li><a href=\"#ndg-step-7\">Deploy model<\/a><\/li><\/ol><div class=\"ndg-step\" id=\"ndg-step-1\"><div class=\"ndg-step__no\">1<\/div><div class=\"ndg-step__body\"><h2>Create approximation model<\/h2><p>Open Neural Designer. The start page is shown.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-create-new-model-2026.png\" width=\"80%\"  alt=\"Start\"\/><\/p>\n<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>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-dataset-editor-2026.png\" width=\"80%\"  alt=\"Dataset Blankt\"\/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-2\"><div class=\"ndg-step__no\">2<\/div><div class=\"ndg-step__body\"><h2>Configure data set<\/h2><p>After creating the model, go to the <em>Data set<\/em> page and click <em>Browse data file<\/em>. For this tutorial, a simple CSV file has been opened as an example.<\/p>\n<p class=\"ndg-download-row\"><a class=\"ndg-download\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/simple-approximation-dataset.csv\" download>Download example dataset<\/a><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-import-data-window-2026.png\" width=\"80%\" alt=\"Neural Designer import data window\"\/><\/p>\n<p>The <em>Import data<\/em> window previews the contents and the detected settings. In this example, the file has a header row, uses a semicolon separator, has no sample index and uses UTF-8 encoding. After checking the preview, click <em>Finish<\/em>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-simple-approximation-dataset-2026.png\" width=\"80%\" alt=\"Simple approximation data set in Neural Designer\"\/><\/p>\n<p>The data set contains 51 samples and two numeric variables, with no missing values:<\/p>\n<ul>\n<li><em>x<\/em> is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#InputVariables\">input variable<\/a>.<\/li>\n<li><em>y<\/em> is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#TargetVariables\">target variable<\/a>.<\/li>\n<\/ul>\n<p>Neural Designer applies the <em>MeanStandardDeviation<\/em> scaler to both variables. The default <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Samples\">sample split<\/a> assigns 41 samples to development and 10 to <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#TestingSamples\">testing<\/a>; no samples are unused.<\/p>\n<p>Select a sample index to inspect its values and role in the lower table.<\/p>\n<p>After configuring the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/\">data set<\/a>, open <em>Task Manager &gt; Data set &gt; Plot scatter charts<\/em>. The Viewer plots the target <em>y<\/em> against the input <em>x<\/em>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-simple-approximation-scatter-chart-2026.png\" width=\"80%\" alt=\"Scatter chart for the simple approximation example\"\/><\/p>\n<p>The parabolic shape represents <em>y = x\u00b2<\/em>. Its linear correlation coefficient is 0 because the relationship is symmetric and nonlinear, illustrating why linear correlation alone cannot reveal every dependency.<\/p>\n<p>You can also evaluate data quality by running additional dataset analysis tasks.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-3\"><div class=\"ndg-step__no\">3<\/div><div class=\"ndg-step__body\"><h2>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 page displays its current layers and architecture.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-network-architecture-editor-2026.png\" width=\"80%\" alt=\"Neural Designer network architecture editor for the simple approximation example.\"\/><\/p>\n<p>The example uses one input feature (<em>x<\/em>) and one target (<em>y<\/em>). Neural Designer creates five layers by default:<\/p>\n<ul>\n<li>One <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ScalingLayer\">scaling layer<\/a> with 1 input and 1 neuron.<\/li>\n<li>Two <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#DenseLayer\">dense layers<\/a>: a hidden layer with 3 neurons and an output layer with 1 neuron.<\/li>\n<li>One <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#UnscalingLayer\">unscaling layer<\/a> with 1 input and 1 output.<\/li>\n<li>One <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ClampingLayer\">clamping layer<\/a> with 1 input and 1 output. Its default method is <em>No clamping<\/em>.<\/li>\n<\/ul>\n<p>The hidden dense layer uses a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#HyperbolicTangentActivationFunction\">hyperbolic tangent activation function<\/a>. The output dense layer uses a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#LinearActivationFunction\">linear activation function<\/a>. The summary reports 1 input feature, 1 output feature, 5 layers and 10 parameters. Keep these default settings.<\/p>\n<p>To visualize the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#NetworkArchitecture\">network architecture<\/a>, run\u00a0<em>Neural network &gt; Report neural network<\/em>\u00a0from the task tree. The Viewer window displays the complete architecture.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-simple-approximation-network-report-2026.png\" width=\"80%\" alt=\"Neural network architecture report for the simple approximation example.\"\/><\/p>\n<p>The report shows <em>x<\/em> entering the scaling layer (yellow), followed by the two dense layers (blue), the unscaling layer (red), and the clamping layer (purple), which produces <em>y<\/em>.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-4\"><div class=\"ndg-step__no\">4<\/div><div class=\"ndg-step__body\"><h2>Train neural network<\/h2>\n<p>Open the <em>Training strategy<\/em> tab. This page configures the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#LossIndex\">loss index<\/a> and the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#OptimizationAlgorithms\">optimization algorithm<\/a>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-training-strategy-editor-2026.png\" width=\"80%\" alt=\"Training strategy configuration for the simple approximation example in Neural Designer.\"\/><\/p>\n<p>For this example, keep the displayed settings:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#MeanSquaredError\">Mean squared error<\/a> as the error method.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#L2Regularization\">L2 regularization<\/a> with a weight of 0.010.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#AdaptiveMomentEstimation\">Adaptive moment estimation (Adam)<\/a> with a batch size of 32 and a learning rate of 0.001.<\/li>\n<li>A maximum of 1000 epochs using the multi-core CPU.<\/li>\n<\/ul>\n<p>Run <em>Training strategy &gt; Perform training<\/em> from the task tree. The optimizer minimizes the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#LossIndex\">loss index<\/a> and adjusts the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">network parameters<\/a> to fit <em>y = x\u00b2<\/em>.<\/p>\n<p>For a practical comparison of gradient descent, quasi-Newton, Levenberg-Marquardt, SGD, Adam and AdamW, see our <a href=\"https:\/\/www.neuraldesigner.com\/blog\/5_algorithms_to_train_a_neural_network\/\">neural network optimizer guide<\/a>.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-5\"><div class=\"ndg-step__no\">5<\/div><div class=\"ndg-step__body\"><h2>Improve generalization performance<\/h2>\n<p>Open the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\">Model selection<\/a> tab to configure neuron and input selection.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-model-selection-editor-2026.png\" width=\"80%\" alt=\"Neuron and input selection configuration in Neural Designer.\"\/><\/p>\n<p>For this example, keep <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#GrowingNeurons\">Growing neurons<\/a> with a minimum of 1 neuron, a maximum of 10, a step of 1 and 3 trials. <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#GrowingInputs\">Input selection<\/a> is not needed because <em>x<\/em> is the only input.<\/p>\n<p>Run <em>Model selection &gt; Perform neuron selection<\/em> from the task tree. The Viewer displays the growing-neurons report.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-neuron-selection-errors-2026.png\" width=\"80%\" alt=\"Training and selection errors for the growing neurons algorithm.\"\/><\/p>\n<p>The training and selection errors decrease rapidly. The minimum selection error is achieved with 6 neurons.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-neuron-selection-results-2026.png\" width=\"80%\" alt=\"Growing neurons results and selected neural network architecture.\"\/><\/p>\n<p>The results report shows 6 optimal neurons, a training error of 0.0171, a selection error of 0.0071 and 10 epochs. Neural Designer updates the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#NetworkArchitecture\">network architecture<\/a> to one hidden layer with 6 neurons and one output neuron. The resulting <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">neural network<\/a> is already trained.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-6\"><div class=\"ndg-step__no\">6<\/div><div class=\"ndg-step__body\"><h2>Test results<\/h2>\n<p>Open <em>Testing analysis<\/em> to evaluate the trained model on the testing samples, which were not used to fit its parameters.<\/p>\n<p>Run <em>Testing analysis &gt; Perform goodness-of-fit analysis<\/em> from the task tree. The Viewer displays the coefficient of determination and the predicted-versus-actual chart.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-goodness-of-fit-analysis-2026.png\" width=\"80%\" alt=\"Goodness-of-fit report with coefficient of determination and predicted versus actual values.\"\/><\/p>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#LinearRegressionAnalysis\">goodness-of-fit analysis<\/a> reports <em>R\u00b2 = 0.937<\/em> for <em>y<\/em>. The blue points compare predicted and actual values, while the grey line represents perfect predictions. Points closer to that line indicate a better fit.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"ndg-step-7\"><div class=\"ndg-step__no\">7<\/div><div class=\"ndg-step__body\"><h2>Deploy model<\/h2>\n<p>Once the model has been tested, open <em>Model deployment<\/em> to generate predictions, analyze its responses and export the model.<\/p>\n<p>Run <em>Model deployment &gt; Plot directional output<\/em> from the task tree. This task varies one input while keeping the others fixed at a reference point.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/neural-designer-directional-output-2026.png\" width=\"80%\" alt=\"Directional output chart showing how y changes as x varies.\"\/><\/p>\n<p>Because this example has only one input, the chart directly shows the response <em>y(x)<\/em> learned by the network. The grey point marks the reference input.<\/p>\n<p>Run <em>Model deployment &gt; Export expression<\/em> to save the complete model as Python code. Instantiate <code>NeuralNetwork<\/code> and call <code>calculate_outputs([...])<\/code> for one prediction or <code>calculate_batch_output(...)<\/code> for a NumPy batch.<\/p>\n<pre>'''\nArtificial Intelligence Techniques SL\nartelnics@artelnics.com\n\nModel exported to Python. Use NeuralNetwork().calculate_outputs([...]).\n\nInput Names:\n\t0) x\n\nFor batch prediction (input must be np.ndarray):\n\tnn.calculate_batch_output(np.array([[1], [4]]))\n'''\nimport math\nimport numpy as np\nimport pandas as pd\n\nclass NeuralNetwork:\n\n\tdef __init__(self):\n\t\tself.inputs_number = 1\n\t\tself.input_names = ['x']\n\n\t@staticmethod\n\tdef Identity(x):\n\t\treturn x\n\n\t@staticmethod\n\tdef Tanh(x):\n\t\treturn np.tanh(x)\n\n\tdef calculate_outputs(self, inputs):\n\t\tx = inputs[0]\n\n\t\tscaled_x = x*0.3396831453+1.905571523e-08\n\t\tdense_layer_1_output_0 = self.Tanh( 1.151840448 + (-1.392787814*scaled_x) )\n\t\tdense_layer_1_output_1 = self.Tanh( -1.355468988 + (-1.329861999*scaled_x) )\n\t\tdense_layer_1_output_2 = self.Tanh( -0.6183619499 + (0.009102747776*scaled_x) )\n\t\tdense_layer_1_output_3 = self.Tanh( -0.3707145751 + (0.01256038155*scaled_x) )\n\t\tdense_layer_1_output_4 = self.Tanh( -0.02022263408 + (-0.03428416327*scaled_x) )\n\t\tdense_layer_1_output_5 = self.Tanh( -0.001989847049 + (0.05457909405*scaled_x) )\n\t\tapproximation_layer_output_0 = self.Identity( 1.264135718 + (-1.791464567*dense_layer_1_output_0) + (1.778391838*dense_layer_1_output_1) + (-0.6996221542*dense_layer_1_output_2) + (-0.3780626953*dense_layer_1_output_3) + (-0.04951105267*dense_layer_1_output_4) + (-0.07944823802*dense_layer_1_output_5) )\n\t\tunscaling_layer_output_0 = approximation_layer_output_0*7.747230053+8.666669846\n\t\ty = unscaling_layer_output_0\n\t\toutputs = [y]\n\t\treturn outputs\n\n\tdef calculate_batch_output(self, input_batch):\n\t\toutput_batch = np.zeros((len(input_batch), 1))\n\t\tfor i in range(len(input_batch)):\n\t\t\tinputs = list(input_batch[i])\n\t\t\toutput = self.calculate_outputs(inputs)\n\t\t\toutput_batch[i] = output\n\t\treturn output_batch\n\ndef main():\n\n\t# Introduce your input values here\n\tx = 0  # x\n\n\t# --- Data conversion (DO NOT modify) ---\n\tinputs = []\n\tinputs.append(x)\n\n\tnn = NeuralNetwork()\n\toutputs = nn.calculate_outputs(inputs)\n\tprint(outputs)\n\nif __name__ == \"__main__\":\n\tmain()\n<\/pre>\n<aside class=\"ndg-cta\" aria-labelledby=\"neural-network-guide-cta-title\" style=\"margin:2rem 0;padding:1.5rem;border:1px solid #d8e2ef;border-radius:12px;background:#f6f9fc;\">\n<h2 id=\"neural-network-guide-cta-title\" style=\"margin-top:0;\">Build your own neural network<\/h2>\n<p>Apply these seven steps to your own data in Neural Designer, from data preparation and architecture selection to testing and model deployment.<\/p>\n<p style=\"display:flex;flex-wrap:wrap;gap:0.75rem;margin-bottom:0;\">\n<a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\" data-nd-cta=\"neural-network-guide-download\" onclick=\"if(typeof gtag==='function'){gtag('event','cta_click',{cta_name:'neural_network_guide_download',link_url:this.href});}\" style=\"display:inline-block;padding:0.75rem 1rem;border-radius:6px;background:#1261a0;color:#fff;text-decoration:none;font-weight:600;\">Download Neural Designer<\/a>\n<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/\" data-nd-cta=\"neural-network-guide-training\" onclick=\"if(typeof gtag==='function'){gtag('event','cta_click',{cta_name:'neural_network_guide_training',link_url:this.href});}\" style=\"display:inline-block;padding:0.75rem 1rem;border:1px solid #1261a0;border-radius:6px;color:#1261a0;text-decoration:none;font-weight:600;\">Review the training strategy<\/a>\n<\/p>\n<\/aside>\n<p>To learn more, see the next example:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/iris-flowers-classification\/\">Classification of iris flowers from sepal and petal dimensions<\/a>.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div>","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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