{"id":3544,"date":"2025-11-26T14:18:07","date_gmt":"2025-11-26T13:18:07","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/technical-features\/"},"modified":"2026-09-15T15:56:48","modified_gmt":"2026-09-15T13:56:48","slug":"technical-features","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/technical-features\/","title":{"rendered":"Neural Designer technical features"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 16px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,1160px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 auto 34px;max-width:820px;text-align:center}.ndb-lead a{color:#2d799f;font-weight:600}.ndtf-grid{display:grid;grid-template-columns:repeat(6,minmax(0,1fr));gap:26px}.ndtf-card{grid-column:span 2;display:flex;flex-direction:column;background:#f2f2f2;border-radius:20px;padding:28px 26px 24px;box-shadow:-12px -12px 24px rgba(255,255,255,.95),12px 12px 24px rgba(30,83,116,.12);transition:transform .2s ease}.ndtf-card:hover{transform:translateY(-5px)}.ndtf-ic{width:66px;height:66px;border-radius:50%;background:#e9f6fd;border:1px solid #cfe9f7;display:flex;align-items:center;justify-content:center;margin:0 0 18px}.ndtf-ic img{width:34px!important;height:34px!important;margin:0!important;box-shadow:none!important;border-radius:0!important;max-width:34px!important}.ndtf-card h3{margin:0 0 12px;color:#001233;font-size:19px;font-weight:600;line-height:1.2}.ndtf-card p{margin:0 0 10px;color:#536273;font-size:14.5px;font-weight:300;line-height:1.5}.ndtf-card ul{margin:0;padding-left:20px;list-style:disc}.ndtf-card li{color:#536273;font-size:14.5px;font-weight:300;line-height:1.5;margin:5px 0}.ndtf-card a{color:#2d799f;font-weight:500}.ndtf-card:nth-child(-n+2){grid-column:span 3}.ndtf-card--wide{grid-column:1\/-1}.ndtf-wide-head{display:flex;align-items:center;gap:18px;margin:0 0 20px}.ndtf-wide-head .ndtf-ic{margin:0}.ndtf-wide-head h3{margin:0}.ndtf-groups{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:34px}.ndtf-group{min-width:0}.ndtf-group h4{margin:0 0 10px;color:#12354b;font-size:16px;font-weight:700;line-height:1.25}@media(max-width:960px){.ndtf-grid{grid-template-columns:repeat(2,minmax(0,1fr))}.ndtf-card,.ndtf-card:nth-child(-n+2){grid-column:auto}.ndtf-card--wide{grid-column:1\/-1}.ndtf-groups{grid-template-columns:repeat(2,minmax(0,1fr))}}@media(max-width:600px){.ndtf-grid{grid-template-columns:1fr}.ndtf-card--wide{grid-column:auto}.ndtf-groups{grid-template-columns:1fr}.ndb{padding:12px 14px}}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><div class=\"ndb-lead\"><p><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 );\">Neural Designer implements the most innovative artificial intelligence techniques.<\/span><\/p>\n<p>Some of the main algorithms it contains are listed below.<\/p>\n<\/div><div class=\"ndtf-grid\"><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/neural_networks_applications.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Application types<\/h3><ul>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Approximation\">Approximation<\/a> (or modeling) to discover intricate relationships.<\/li>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Classification\">Classification<\/a> (or pattern recognition) to recognize complex patterns.<\/li>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Forecasting\">Forecasting<\/a> (or time series prediction) to predict trends.<\/li>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#AnomalyDetection\">Anomaly detection<\/a> (using auto-associative networks) to flag unusual samples.<\/li>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#ImageClassification\">Image classification<\/a> (or image recognition) to identify visual categories.<\/li>\n<li style=\"text-align: left;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#TextClassification\">Text classification<\/a> (or document classification) to categorize texts.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/data_set.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Data set<\/h3><ul>\n<li>Compatible with the most common <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#DataSource\">data files<\/a>: CSV, DAT, TXT, Excel and OpenOffice.<\/li>\n<li>Complete configuration of numerical, binary, categorical, date-time and constant <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Variables\">variables<\/a>.<\/li>\n<li>Complete configuration of training, validation, testing and unused <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Samples\">samples<\/a>.<\/li>\n<li>Random and sequential sample splitting.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#MissingValues\">Missing values<\/a> exclusion and imputation.<\/li>\n<li>Numerical and image data scaling.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Statistics\">Descriptive statistics<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Distributions\">distributions<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#BoxPlots\">box plots<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#TimeSeriesPlots\">time series plots<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#ScatterCharts\">scatter charts<\/a>.<\/li>\n<li>Variable importance using <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#InputsCorrelations\">input<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#InputsTargetsCorrelations\">input-target correlations<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Autocorrelations\">Autocorrelations<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#CrossCorrelations\">cross-correlations<\/a> for time series.<\/li>\n<li>Utilities for <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Outliers\">outlier detection<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Filtering\">data filtering<\/a>.<\/li>\n<li>Text data preprocessing.<\/li>\n<li>Image resizing and data augmentation.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card ndtf-card--wide\"><div class=\"ndtf-wide-head\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/neural_network.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Neural network<\/h3><\/div><div class=\"ndtf-groups\"><div class=\"ndtf-group\"><h4>Network architectures<\/h4><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#NetworkArchitecture\">Network architecture<\/a> with unlimited layers.<\/li>\n<li>Dense networks for approximation and classification.<\/li>\n<li>Recurrent networks for forecasting.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ConvolutionalNeuralNetworks\">Convolutional networks<\/a> for image classification.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#AutoAssociativeNeuralNetworks\">Auto-associative networks<\/a> for anomaly detection.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#AttentionNeuralNetworks\">Attention networks<\/a> for text classification.<\/li>\n<\/ul><\/div><div class=\"ndtf-group\"><h4>Layers<\/h4><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#DenseLayer\">Dense layers<\/a> for fully connected transformations and classification outputs.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ConvolutionalLayer\">Convolutional layers<\/a> for spatial feature extraction.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#PoolingLayer\">Pooling layers<\/a> for spatial and sequence summarization.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#EmbeddingLayer\">Embedding layers<\/a> for token representations.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#MultiHeadAttentionLayer\">Multi-head attention layers<\/a> for contextual representations.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#LongShortTermMemoryLayer\">Long short-term memory layers<\/a> for temporal dependencies.<\/li>\n<\/ul><\/div><div class=\"ndtf-group\"><h4>Functions and transformations<\/h4><ul>\n<li>Dense activations: <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#LinearActivationFunction\">linear<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#HyperbolicTangentActivationFunction\">hyperbolic tangent<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#RectifiedLinearActivationFunction\">rectified linear<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#SigmoidActivationFunction\">sigmoid<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#SoftmaxActivationFunction\">softmax<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ConvolutionalLayer\">Convolutions<\/a>: <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#SameConvolution\">same<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ValidConvolution\">valid<\/a>, with configurable filters, kernel size and stride.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#PoolingLayer\">Pooling<\/a>: <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#MaxPooling\">maximum<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#AveragePooling\">average<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#EmbeddingLayer\">Embeddings<\/a> with configurable vocabulary, sequence length and embedding dimension.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#MultiHeadAttentionLayer\">Multi-head attention<\/a> with configurable attention heads.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ScalingLayer\">Scaling<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#UnscalingLayer\">unscaling<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/#ClampingLayer\">clamping<\/a> transformations.<\/li>\n<\/ul><\/div><\/div><\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/training_strategy.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Training strategy<\/h3><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#Loss\">Loss functions<\/a>:\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#MeanSquaredError\">Mean squared error<\/a> for approximation and forecasting.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#MeanAbsoluteError\">Mean absolute error<\/a> for anomaly detection and robust regression; <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#MinkowskiError\">Minkowski error<\/a> for robust regression.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#NormalizedSquaredError\">Normalized squared error<\/a> for variance-normalized regression.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#WeightedSquaredError\">Weighted squared error<\/a> for imbalanced binary classification.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#CrossEntropyError\">Cross-entropy<\/a> for binary and multiclass classification.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#CrossEntropyError3d\">3D cross-entropy<\/a> for language modeling.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#RegularizationTerm\">Optional L1 and L2 regularization<\/a> for controlling complexity.<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#OptimizationAlgorithms\">Optimization algorithms<\/a>:\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#AdaptiveMomentEstimation\">Adam<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#StochasticGradientDescent\">SGD<\/a> for mini-batch CPU and GPU training.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#QuasiNewtonMethod\">Quasi-Newton<\/a> for full-batch tabular models on CPU.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#LevenbergMarquardtAlgorithm\">Levenberg-Marquardt<\/a> for small sequential Dense models on CPU.<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/#TrainingControl\">Training control<\/a>: automatic batch sizing, validation, stopping criteria, gradient clipping and best-model restoration.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model_selection.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Model selection<\/h3><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#OrderSelection\">Neurons selection algorithm<\/a> for finding the optimal network architecture: <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#IncrementalOrder\">incremental order<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#InputsSelection\">Inputs selection algorithms<\/a> for selecting the most important features: <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#GrowingInputs\">growing inputs<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#PrunningInputs\">pruning inputs<\/a>, and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#GeneticAlgorithm\">genetic algorithm<\/a>.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/testing_analysis.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Testing analysis<\/h3><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#TestingErrors\">Testing errors<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#ErrorsStatistics\">statistics<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#ErrorsHistogram\">histograms<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#MaximalErrors\">maximal errors<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#LinearRegressionAnalysis\">Goodness-of-fit analysis<\/a> for approximation and forecasting.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#OutputsPlot\">Outputs plots<\/a> for forecasting.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#ConfusionMatrix\">Confusion matrices<\/a> for binary and multiple classification.<\/li>\n<li>Full metrics for <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#BinaryClassificationTests\">binary<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#MultipleClassificationTests\">multiple classification<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#RocCurve\">ROC curve<\/a> and optimal threshold.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#CumulativeGain\">Cumulative gain<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#LiftChart\">lift<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#PositivesNegativesRates\">rates<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#ProfitChart\">Profit chart<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/#MisclassifiedInstances\">misclassified samples<\/a>.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model_deployment.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Model deployment<\/h3><ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#NeuralNetworkOutputs\">Output values<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#OutputData\">output data<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#DirectionalOutputs\">Directional outputs<\/a> for model exploration.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#SampleInputImportances\">Sample<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#ModelInputImportances\">model input importances<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#ResponseOptimization\">Response optimization<\/a> under given conditions.<\/li>\n<li>Exportable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#MathematicalExpression\">mathematical expression<\/a>.<\/li>\n<li>Exportable model in <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#CExpression\">C<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#PythonExpression\">Python<\/a>, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#ProgrammingLanguageExpressions\">JavaScript and PHP<\/a>.<\/li>\n<li>Portable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/#DeploymentPackage\">deployment packages<\/a> for image and text classification.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/icon-output.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Output<\/h3><ul>\n<li>Interactive reports with descriptions, tables and charts.<\/li>\n<li>Reports printable and exportable to ODT and PDF.<\/li>\n<li>Text, tables and chart data exportable to TXT and CSV.<\/li>\n<li>Tables, charts and network diagrams exportable to PNG.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/icon-help.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Help<\/h3><ul>\n<li>Practical <a href=\"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/user-guide\/\">user&#8217;s guide<\/a>.<\/li>\n<li>Extensive <a href=\"https:\/\/www.neuraldesigner.com\/learning\/\">machine learning tutorials<\/a>.<\/li>\n<li>Step-by-step solved <a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/examples\/\">examples<\/a> in different fields.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/faq\/\">Frequently asked questions<\/a> and <a href=\"https:\/\/www.youtube.com\/@neural.designer\">video tutorials<\/a>.<\/li>\n<li>Premium <a href=\"https:\/\/www.neuraldesigner.com\/contactus\/\">technical support<\/a> by email, phone or video call.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/icon-performance.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Performance<\/h3><ul>\n<li>High-performance C++ implementation.<\/li>\n<li>Optimized memory management and numerical operations.<\/li>\n<li>Multicore CPU parallelization with OpenMP and Eigen.<\/li>\n<li>NVIDIA GPU acceleration with CUDA, cuBLAS and cuDNN.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/icon-platforms.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Supported platforms<\/h3><ul>\n<li>Windows 64-bit.<\/li>\n<li>macOS for Apple silicon (M Series).<\/li>\n<li>Ubuntu Linux 64-bit.<\/li>\n<\/ul>\n<\/article><article class=\"ndtf-card\"><span class=\"ndtf-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/icon-cloud.svg\" alt=\"\" loading=\"lazy\"><\/span><h3>Cloud computing<\/h3><ul>\n<li>Run Neural Designer on <a href=\"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/tutorial-aws\/\">Amazon Web Services (AWS)<\/a>.<\/li>\n<\/ul>\n<\/article><\/div><\/div><\/div>\n","protected":false},"author":122,"featured_media":2755,"template":"","categories":[31],"tags":[36],"class_list":["post-3544","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>Neural Designer technical features<\/title>\n<meta name=\"description\" content=\"Discover the most innovative technical features of the explainable machine learning platform Neural Designer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/technical-features\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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