{"id":3537,"date":"2025-11-24T11:12:57","date_gmt":"2025-11-24T10:12:57","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/neural-networks-tutorial\/"},"modified":"2026-02-11T09:52:12","modified_gmt":"2026-02-11T08:52:12","slug":"neural-networks-tutorial","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-tutorial\/","title":{"rendered":"Neural Networks Tutorial: Architecture &#038; Learning"},"content":{"rendered":"<style>.ndg{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 12px;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 24px}.ndg-lead a{color:#2d799f;font-weight:600}.ndg-herofig{margin:0 0 30px;text-align:center}.ndg-herofig 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img:not([src$=\".svg\"]):not([data-src$=\".svg\"]){display:block!important;width:auto!important;max-width:min(580px,100%)!important;height:auto!important;margin:22px auto!important;border-radius:12px!important;box-shadow:0 12px 28px rgba(0,18,51,.12)!important}.ndg-step__body img[src$=\".svg\"],.ndg-step__body img[data-src$=\".svg\"]{display:block;margin:18px auto;max-width:min(520px,100%);height:auto}.ndg-step__body table{border-collapse:separate;border-spacing:0;margin:20px 0;font-size:15px;width:auto;max-width:100%;background:#fbfcfd;border-radius:12px;overflow:hidden;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndg-step__body th,.ndg-step__body td{padding:11px 18px;border-bottom:1px solid #e6ecf0;text-align:left}.ndg-step__body th{background:#e9f1f6;color:#12354b;font-weight:600}.ndg-step__body td{color:#33424f}.ndg-step__body pre{margin:22px 0;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:1.55;white-space:pre}.ndg-nav{display:flex;justify-content:space-between;gap:16px;margin:12px 0 8px;flex-wrap:wrap}.ndg-nav a{display:inline-flex;align-items:center;padding:13px 24px;border-radius:10px;background:#f2f2f2;color:#2d799f!important;font-weight:800;font-size:15px;box-shadow:-8px -8px 16px rgba(255,255,255,.92),8px 8px 16px rgba(30,83,116,.10)}.ndg-nav a:hover{color:#1f5f80!important}@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;flex:0 0 52px;font-size:22px}.ndg-step__body h2{font-size:22px}}@media(max-width:640px){.ndg{padding:12px 14px}}<\/style><div class=\"ndg\"><div class=\"ndg-wrap\"><div class=\"ndg-lead\"><p>\u00a0\u00a0<\/p>\n<p data-start=\"146\" data-end=\"292\">In this neural networks tutorial, we introduce neural networks as the most important technique for <a href=\"https:\/\/www.ibm.com\/think\/topics\/machine-learning\">machine learning<\/a> and artificial intelligence.<\/p>\n<p data-start=\"299\" data-end=\"405\" data-is-last-node=\"\">They are dramatically improving the state-of-the-art in energy, marketing, health, and many other domains.<\/p>\n<p>This tutorial describes the principal applications and concepts related to neural networks.<\/p>\n<\/div><div class=\"ndg-herofig\"><img decoding=\"async\" style=\"width: 382px; max-width: 100%;\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural_network_graph.svg\" height=\"237\" \/><\/div><p class=\"ndg-eyebrow\">Tutorial index<\/p><ul class=\"ndg-toc\"><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\">Model types<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">Data set<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">Neural network<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\">Training strategy<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">Model selection<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">Testing analysis<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\">Model deployment<\/a><\/li><\/ul><div class=\"ndg-step\" id=\"model-types\"><div class=\"ndg-step__no\">1<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\" style=\"color:#001233;font-weight:700\">Model types<\/a><\/h2><p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural_networks_applications.svg\" width=\"49\" height=\"49\" alt=\"\" loading=\"lazy\"><\/p><p>The most common learning tasks for neural networks are approximation, classification, and forecasting.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data_set.svg\" width=\"49\" height=\"49\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"data-set\"><div class=\"ndg-step__no\">2<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\" style=\"color:#001233;font-weight:700\">Data set<\/a><\/h2><p>The data set contains the information available for creating the model.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural_network.svg\" width=\"46\" height=\"46\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"neural-network\"><div class=\"ndg-step__no\">3<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\" style=\"color:#001233;font-weight:700\">Neural network<\/a><\/h2><p>The neural network represents the approximation or classification model.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training_strategy.svg\" width=\"45\" height=\"45\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"training-strategy\"><div class=\"ndg-step__no\">4<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\" style=\"color:#001233;font-weight:700\">Training strategy<\/a><\/h2><p>The training strategy fits the neural network with the data set.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model_selection.svg\" width=\"45\" height=\"45\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"model-selection\"><div class=\"ndg-step__no\">5<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\" style=\"color:#001233;font-weight:700\">Model selection<\/a><\/h2><p>Model selection algorithms look for the neural network architecture with the best generalization capabilities.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/testing_analysis.svg\" width=\"46\" height=\"46\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"testing-analysis\"><div class=\"ndg-step__no\">6<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\" style=\"color:#001233;font-weight:700\">Testing analysis<\/a><\/h2><p>The testing analysis compares the outputs from the neural network against the targets in an independent set.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model_deployment.svg\" width=\"49\" height=\"49\" \/><\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"model-deployment\"><div class=\"ndg-step__no\">7<\/div><div class=\"ndg-step__body\"><h2><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\" style=\"color:#001233;font-weight:700\">Model deployment<\/a><\/h2><p>Model deployment is used to apply a model to predict new data.<\/p>\n<p>In the following pages, we describe each of these concepts in detail.<a style=\"float: right;\" href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\">\u00a0Model Types \u203a<\/a><\/p>\n<\/div><\/div><\/div><\/div>","protected":false},"author":122,"featured_media":2296,"template":"","categories":[30],"tags":[36],"class_list":["post-3537","learning","type-learning","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>Neural Networks Tutorial: Architecture &amp; Learning<\/title>\n<meta name=\"description\" content=\"Comprehensive neural networks tutorial covering architecture, components, and learning process in machine learning 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