{"id":163759,"date":"2023-08-08T13:59:17","date_gmt":"2023-08-08T12:59:17","guid":{"rendered":"https:\/\/datascientest.com\/de\/?p=163759"},"modified":"2023-10-30T14:44:25","modified_gmt":"2023-10-30T13:44:25","slug":"neuronale-netze-einfach-erklaert","status":"publish","type":"post","link":"https:\/\/datascientest.com\/de\/neuronale-netze-einfach-erklaert","title":{"rendered":"Neuronale Netze einfach erkl\u00e4rt"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"163759\" class=\"elementor elementor-163759\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b679dd2 e-flex e-con-boxed e-con\" data-id=\"b679dd2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-71f95ba elementor-widget elementor-widget-text-editor\" data-id=\"71f95ba\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h3>Hier<strong> findest Du unseren vierten Artikel \u00fcber Deep Learning. Lies die ersten Artikel, um Deine Kenntnisse \u00fcber<a href=\"https:\/\/datascientest.com\/definition-deep-learning\/#introduction\"><span style=\"text-decoration: underline;\"> Deep Learning, Machine Learning und K\u00fcnstlicher Intelligenz<\/span><\/a> sowie \u00fcber die <a href=\"https:\/\/datascientest.com\/de\/deep-neural-network\"><span style=\"text-decoration: underline;\">Funktionsweise und Anwendungen von neuronalen Netzwerken <\/span><\/a>aufzufrischen. Lies auch noch einmal den Artikel \u00fcber das CNN: Convolutional Neural Network. Und jetzt los mit unserem heutigen Thema !\u00a0<\/strong><\/h3>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0694ec1 e-flex e-con-boxed e-con\" data-id=\"0694ec1\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3e869d3 elementor-widget elementor-widget-heading\" data-id=\"3e869d3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">Inhaltsverzeichnis:<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fc1ded elementor-widget elementor-widget-text-editor\" data-id=\"5fc1ded\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol><li style=\"font-weight: 400\"><strong><a href=\"https:\/\/datascientest.com\/de\/deep-learning-oder-tiefes-lernen-was-ist-das-denn\"><span style=\"text-decoration: underline\">Einf\u00fchrung in Deep Learning<\/span><\/a><\/strong><\/li><li style=\"font-weight: 400\"><a href=\"https:\/\/datascientest.com\/de\/deep-learning-oder-tiefes-lernen-was-ist-das-denn\"><span style=\"font-weight: 400\"><strong><span style=\"text-decoration: underline\">Neuronale Netzwerke: Biologisch VS K\u00fcnstlich<\/span><\/strong><\/span><\/a><\/li><li style=\"font-weight: 400\"><a href=\"https:\/\/datascientest.com\/de\/convolutional-neural-network-2\"><span style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><strong>Konvolutionale neuronale Netze<\/strong><\/span><\/span><\/a><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Funktionsweise von neuronalen Netzen<\/span><ol><li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><strong><a href=\"#neuronalenetze\">Neuronale Netze (Auffrischung)<\/a><\/strong><\/span><\/li><li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><strong><a href=\"#gradientdescending\">Gradient Descending<\/a><\/strong><\/span><\/li><\/ol><\/li><li style=\"font-weight: 400\"><a href=\"https:\/\/datascientest.com\/de\/transfer-learning\"><span style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><strong>Transfer learning<\/strong><\/span><\/span><\/a><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0bd10fb e-flex e-con-boxed e-con\" data-id=\"0bd10fb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-224de27 elementor-widget elementor-widget-text-editor\" data-id=\"224de27\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Nun wirst Du im Detail erfahren, wie <\/span><b>k\u00fcnstliche neuronale Netze<\/b><span style=\"font-weight: 400\"> lernen und wie sie <\/span><b>sich in einem Netz artikulieren<\/b><span style=\"font-weight: 400\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9acdc0b elementor-widget elementor-widget-menu-anchor\" data-id=\"9acdc0b\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\nbody.elementor-page .elementor-widget-menu-anchor{margin-bottom:0}<\/style>\t\t<div id=\"neuronalenetze\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5994a86 e-flex e-con-boxed e-con\" data-id=\"5994a86\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-99a27f8 elementor-widget elementor-widget-heading\" data-id=\"99a27f8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Neuronale Netze - eine kurze Auffrischung<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2f9d42 elementor-widget elementor-widget-text-editor\" data-id=\"a2f9d42\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Multilayer perceptron <\/b><span style=\"font-weight: 400\">(mehrlagiges Perzeptron) ist ein Stapel von Perzeptronen, die in eine Schicht gelegt werden. Nehmen wir das folgende Beispiel, um das Konzept zu veranschaulichen:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e650d4 elementor-widget elementor-widget-image\" data-id=\"7e650d4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"234\" src=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/DP_2.png\" class=\"attachment-large size-large wp-image-4624\" alt=\"Deep learning neuronnes\" srcset=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/DP_2.png 512w, https:\/\/datascientest.com\/de\/files\/2020\/06\/DP_2-300x137.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1f18a6 elementor-widget elementor-widget-text-editor\" data-id=\"a1f18a6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Das Modell besteht aus der Klassifizierung (in 10 Klassen) von Bildern handgeschriebener Zahlen. Die gr\u00fcnen Quadrate sind die Eingabeschicht unseres Modells, d. h. die Werte der Pixel. Die mittlere und die Ausgabeschicht sind Perzeptrons, die durch graue runde Kreise dargestellt werden. Die letzte Schicht hat 10 Neuronen, da es 10 Klassen gibt, und gibt die entsprechende Wahrscheinlichkeit f\u00fcr jede Klasse zur\u00fcck.<\/span><\/p><p><span style=\"font-weight: 400\">Wir k\u00f6nnen das Modell sehr einfach in Tensorflow implementieren:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ac919b4 elementor-widget elementor-widget-image\" data-id=\"ac919b4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"230\" src=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/DP.png\" class=\"attachment-large size-large wp-image-4623\" alt=\"Tensorflow\" srcset=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/DP.png 512w, https:\/\/datascientest.com\/de\/files\/2020\/06\/DP-300x135.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-58fca58 e-flex e-con-boxed e-con\" data-id=\"58fca58\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0f02bdc elementor-widget elementor-widget-heading\" data-id=\"0f02bdc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Trainieren des Modells<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc5c4b4 elementor-widget elementor-widget-text-editor\" data-id=\"bc5c4b4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Um unser Modell zu trainieren, d.h. die passenden Gewichte zu finden, m\u00fcssen wir eine Funktion definieren, <\/span><b>die den Fehler unseres Modells quantifiziert.<\/b><span style=\"font-weight: 400\"> Normalerweise verwenden wir bei Klassifikationsproblemen die Funktion &#8222;cross-entropy&#8220;, um den Fehler zu messen.<\/span><\/p><p><span style=\"font-weight: 400\">Unser Optimierungsproblem besteht darin, die Gewichte unseres Modells zu finden, die unsere Fehlerfunktion minimieren:<\/span><\/p><pre style=\"padding-left: 480px\"><span style=\"font-weight: 400\">J(w)<\/span><\/pre><p><span style=\"font-weight: 400\">Mit Deep Learning sind wir mit <\/span><b>komplexen Minimierungsproblemen konfrontiert,<\/b><span style=\"font-weight: 400\"> f\u00fcr die es <\/span><b>keine explizite L\u00f6sung<\/b><span style=\"font-weight: 400\"> gibt. Daher werden Optimierungsmethoden wie der <\/span><b>Gradientenabstieg <\/b><span style=\"font-weight: 400\">verwendet, um sie zu <\/span><b>l\u00f6sen<\/b><span style=\"font-weight: 400\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10edaad elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"10edaad\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/de\/unsere-aus-und-weiterbildungen\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Neuronale Netze lernen<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-096beaa e-flex e-con-boxed e-con\" data-id=\"096beaa\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-83129d2 elementor-widget elementor-widget-heading\" data-id=\"83129d2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Gradientenabstieg: Um abzusteigen, muss man nach unten gehen<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0452e12 elementor-widget elementor-widget-menu-anchor\" data-id=\"0452e12\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"gradientdescending\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52f8619 elementor-widget elementor-widget-text-editor\" data-id=\"52f8619\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Um das Konzept des Gradientenabstiegs auf intuitive Weise kennenzulernen, kannst Du unser Video zu diesem Thema schauen:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4fc7bed elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"4fc7bed\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=IHZwWFHWa-w&amp;t=1s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-988dae6 elementor-widget elementor-widget-text-editor\" data-id=\"988dae6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">In unserem obigen Beispiel kann man sich die <\/span><b>Verlustfunktion<\/b><span style=\"font-weight: 400\"> (Fehlerfunktion) als einen Berg vorstellen. Die <\/span><b>Koordinaten<\/b><span style=\"font-weight: 400\"> (Modellgewichte) des Punktes mit der geringsten H\u00f6he\u00a0 werden gesucht. Der Wert des Gradienten der Verlustfunktion entspricht der Richtung und Winkel des Hangs des Berges an einem bestimmten Punkt:<\/span><\/p><ul><li><span style=\"font-weight: 400\">Wenn die <\/span><b>Norm des Gradienten<\/b><span style=\"font-weight: 400\"> hoch ist, ist der Hang sehr steil.<\/span><\/li><li><span style=\"font-weight: 400\">Wenn die Norm des Gradienten niedrig ist, ist der Hang nur wenig geneigt.<\/span><\/li><li><span style=\"font-weight: 400\">Wenn die Gradientennorm null ist, befindet man sich auf einer flachen Ebene (Minimum).<\/span><\/li><\/ul><p><span style=\"font-weight: 400\">Daher besteht der Gradientenabstieg anf\u00e4nglich darin, die Gewichte zuf\u00e4llig zu w\u00e4hlen und die Inverse des Gradienten bei jeder Iteration zu verfolgen:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2aa1288 elementor-widget elementor-widget-image\" data-id=\"2aa1288\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/medium.com\/@acb28\/implement-linear-regression-using-gradient-descent-9209e45273ce\" target=\"_blank\" rel=\"nofollow\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"379\" height=\"284\" src=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/descente-de-gradient.gif\" class=\"attachment-large size-large wp-image-4685\" alt=\"descente de gradient\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94a69ec elementor-widget elementor-widget-image\" data-id=\"94a69ec\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"193\" height=\"52\" src=\"https:\/\/datascientest.com\/de\/files\/2020\/06\/DP_fonctionnement.png\" class=\"attachment-large size-large wp-image-4622\" alt=\"Deep learning\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d0a28ba elementor-widget elementor-widget-text-editor\" data-id=\"d0a28ba\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Mit:<\/span><\/p><ul><li><span style=\"font-weight: 400\">Wt: die Parameter unseres Modells<\/span><\/li><li><span style=\"font-weight: 400\">\u03b7: Schrittweite des Gradienten, d. h. die Lernrate.<\/span><\/li><li><span style=\"font-weight: 400\">J: Verlustfunktion<\/span><\/li><\/ul><p><b>Mit einer zu gro\u00dfem oder zu kleinem Schrittweite wird der MLP-Algorithmus zu keiner zufriedenstellenden L\u00f6sung konvergieren<\/b><span style=\"font-weight: 400\">. Die richtige Schrittweise zu finden, ist die ganze Herausforderung des Deep Learning.<\/span><\/p><p><span style=\"font-weight: 400\">Zusammenfassend kann man sagen, dass sich das k\u00fcnstliche Neuron, auch Perzeptron genannt, wie ein lineares Klassifikationsmodell verh\u00e4lt. Das mehrschichtige Perzeptron hingegen verbindet und stapelt Perzeptrons in Form von Schichten, um die Komplexit\u00e4t der Regelerstellung zu erh\u00f6hen.<\/span><\/p><p><span style=\"font-weight: 400\">Nachdem wir die Parameter des Modells definiert haben, haben wir gesehen, dass es notwendig ist, den Fehler des Modells zu quantifizieren. Um dies zu erreichen, verwendet Deep Learning haupts\u00e4chlich Gradientenabstieg, um die Gewichte so anzupassen, dass die Verlustfunktion minimiert wird.<\/span><\/p><p><span style=\"font-weight: 400\">Um die Mathematik hinter diesem Gradientenabstiegsalgorithmus und die Bedeutung der Lernrate zu erforschen, kannst Du <\/span><span style=\"text-decoration: underline\"><strong><a href=\"https:\/\/datascientest.com\/de\/gradient-boosting-algorithmen\">diesen Artikel<\/a><\/strong><\/span><span style=\"font-weight: 400\"> lesen.<\/span><\/p><p><span style=\"font-weight: 400\">Im Folgenden stellen wir Dir Transfer Learning vor, eine erfolgreiche Lernkategorie, die in Deep Learning verwendet wird und sich stark am menschlichen Lernen zu orientieren scheint!<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-40e0fed e-flex e-con-boxed e-con\" data-id=\"40e0fed\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-374cd6e elementor-widget elementor-widget-text-editor\" data-id=\"374cd6e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol><li><span style=\"font-weight: 400\">Artikel: <\/span><strong><a href=\"https:\/\/datascientest.com\/de\/deep-learning-oder-tiefes-lernen-was-ist-das-denn\">Deep Learning oder Tiefes Lernen: Was ist das denn?<\/a><\/strong><\/li><li><span style=\"font-weight: 400\">Artikel: <\/span><a href=\"https:\/\/datascientest.com\/de\/convolutional-neural-network-2\">Convolutional Neural Network<\/a><\/li><li><span style=\"font-weight: 400\">Artikel: <\/span><span style=\"text-decoration: underline\"><strong><a href=\"https:\/\/datascientest.com\/de\/transfer-learning\">Transfert Learning<\/a><\/strong><\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c740404 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"c740404\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/de\/weiterbildung-deep-learning\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Unsere Weiterbildung in Deep Learning entdecken<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c0e1c16 elementor-widget elementor-widget-text-editor\" data-id=\"c0e1c16\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>\ud83d\udca1 Auch interessant:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><colgroup><col width=\"426\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Deep Learning - was ist das eigentlich ?&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/de\/deep-learning-oder-tiefes-lernen-was-ist-das-denn\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/de\/deep-learning-oder-tiefes-lernen-was-ist-das-denn\" target=\"_blank\" rel=\"noopener\">Deep Learning &#8211; was ist das eigentlich ?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Deep Fake Gefahren&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/de\/deep-fake-gefahren-massnahmen-und-rechtslage\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/de\/deep-fake-gefahren-massnahmen-und-rechtslage\" target=\"_blank\" rel=\"noopener\">Deep Fake Gefahren<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Python Deep Learning Basics&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/de\/python-deep-learning-die-basics\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/de\/python-deep-learning-die-basics\" target=\"_blank\" rel=\"noopener\">Python Deep Learning Basics<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Style Transfer Deep Learning&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/de\/style-transfer-deep-learning\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/de\/style-transfer-deep-learning\" target=\"_blank\" rel=\"noopener\">Style Transfer Deep Learning<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Alphacode Deepminds&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/de\/alphacode-deepminds-neues-ki-tool-zum-schreiben-von-computercode\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/de\/alphacode-deepminds-neues-ki-tool-zum-schreiben-von-computercode\" target=\"_blank\" rel=\"noopener\">Alphacode Deepminds<\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Hier findest Du unseren vierten Artikel \u00fcber Deep Learning. Lies die ersten Artikel, um Deine Kenntnisse \u00fcber Deep Learning, Machine Learning und K\u00fcnstlicher Intelligenz sowie \u00fcber die Funktionsweise und Anwendungen von neuronalen Netzwerken aufzufrischen. Lies auch noch einmal den Artikel \u00fcber das CNN: Convolutional Neural Network. Und jetzt los mit unserem heutigen Thema !&nbsp; Inhaltsverzeichnis: [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":163761,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2454],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Neuronale Netze einfach erkl\u00e4rt<\/title>\n<meta name=\"description\" content=\"Erfahre, wie neuronale Netze funktionieren und welche Bedeutung sie f\u00fcr die Datenwissenschaft und das Deep Learning haben.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/de\/neuronale-netze-einfach-erklaert\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neuronale Netze einfach erkl\u00e4rt\" \/>\n<meta property=\"og:description\" content=\"Hier findest Du unseren vierten Artikel \u00fcber Deep Learning. 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