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`http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/de/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2586836773","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-394752517d","text":"\u003Cp\u003EDeep Learning ist eine Untergruppe des maschinellen Lernens, die sich die Leistungsfähigkeit künstlicher neuronaler Netze zunutze macht, um die komplexen Muster, die sich in Rohdaten verstecken, automatisch zu entdecken und zu modellieren. Es ist zur Triebfeder \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-programming-languages/\"\u003Emoderner KI-\u003C/a\u003ESysteme geworden, hat Durchbrüche bei der Bilderkennung und der Verarbeitung natürlicher Sprache ausgelöst und generiert überzeugend menschenähnliche Texte, die KI-Chatbots unterstützen. Deep Learning bietet auch die Grundlage für autonome Technologien wie selbstfahrende Fahrzeuge und intelligente Roboter, die Sensorströme in Echtzeit verarbeiten, um die Welt wahrzunehmen und Entscheidungen in Sekundenbruchteilen zu treffen.\u003C/p\u003E\n\u003Cp\u003EIn diesem Leitfaden erfahren Sie, was Deep Learning ist und warum es wichtig ist, sowie welche Vorteile und Grenzen es bietet.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0550f18a39","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Deep Learning?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b4f2d074aa","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDeep Learning ist eine fortschrittliche Art des \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emaschinellen Lernens\u003C/a\u003E, bei der mehrschichtige neuronale Netze verwendet werden, um komplexe Muster direkt aus \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003ERohdaten\u003C/a\u003E automatisch zu lernen. Im Gegensatz zu herkömmlichen Algorithmen für maschinelles Lernen müssen Menschen ihr nicht sagen, auf welche Merkmale sie achten sollten, wie Kanten und Farben innerhalb eines Bildes oder übliche Wortmuster im Text. Deep Learning basiert stattdessen auf Netzwerken mit vielen Ebenen künstlicher Neuronen, die automatisch herausfinden, welche dieser Funktionen wichtig sind. Dieser selbstlernende Prozess erfordert viel größere Trainings-Datasets, um sicherzustellen, dass das \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003EModell\u003C/a\u003E Muster innerhalb der Daten wirklich versteht und nicht nur auswendig lernt. Und da die meisten neuronalen Netze auf Dutzenden verschiedener Rechenebenen basieren, die alle gleichzeitig berechnen, benötigt Deep Learning auch deutlich mehr Rechenleistung als herkömmliche Algorithmen für maschinelles Lernen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_2060034519":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-f79ff75d98","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum ist Deep Learning so wichtig?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-73c77a9cc2","text":"\u003Cp\u003EDeep Learning ermöglicht es Unternehmen, aussagekräftige Muster automatisch aus unstrukturierten Daten zu extrahieren und bisher unmögliche oder unpraktische Aufgaben wie Echtzeit-Betrugserkennung, medizinische Bildanalyse und Warehouse-Robotik zu automatisieren. Unternehmen, die Deep Learning beherrschen, erhalten die Fähigkeit, ungenutzte Daten zu verarbeiten, komplexe Workflows zu automatisieren und Marktchancen schneller als Mitbewerber zu erkennen. Das ist für die langfristige strategische Positionierung in einer zunehmend datengestützten Wirtschaft unerlässlich.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-b6811871ad","additionalClasses":"headline-decoration","type":"heading2","lines":["Deep-Learning-Beispiele und -Anwendungsfälle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2e297baed9","text":"\u003Cp\u003EDeep-Learning-Modelle sind bereits in verschiedensten Branchen im Einsatz. Hier sind nur einige Beispiele:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EBetrugserkennung im Finanzwesen\u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Systeme analysieren Transaktionsmuster in Echtzeit, um verdächtige Aktivitäten zu identifizieren, die vom typischen Kundenverhalten abweichen. Diese Modelle können hochriskante Transaktionen zur Überprüfung kennzeichnen oder automatisch blockieren, was Betrugsverluste reduzieren und Kunden vor unbefugten Gebühren schützen kann. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVorausschauende Wartung in der Fertigung\u003C/h3\u003E\r\n\u003Cp\u003EDeep Learning analysiert Sensordaten von Industriemaschinen – wie Vibrationen, Temperatur- und akustische Signale –, um Warnsignale für einen drohenden Geräteausfall zu erkennen. Dank dieser Prognosefunktion können Fertigungsunternehmen Wartungsarbeiten während geplanter Ausfallzeiten planen, kostspielige Ausfälle drastisch reduzieren und die Lebensdauer der Geräte verlängern und gleichzeitig die Wartungskosten optimieren.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EPersonalisierte Empfehlungen im Handel\u003C/h3\u003E\r\n\u003Cp\u003EE-Commerce-Plattformen nutzen Deep Learning, um den Browserverlauf, das Kaufverhalten und die Ähnlichkeit von Kund:innen mit anderen Kund:innen zu analysieren. So können sie ihnen andere Produkte empfehlen, die für die Kund:innen interessant sein könnten. Indem Deep Learning personalisiertere Vorschläge für Kund:innen präsentiert, kann es das Kundenengagement steigern und die Konversionsraten je nach Implementierung und Kontext verbessern. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMedizinische Bildgebung und Diagnostik\u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Modelle, die mit Millionen von medizinischen Bildern trainiert wurden – darunter Röntgenbilder, CT-Scans, MRTs und Netzhautaufnahmen –, können Krankheiten wie Krebs, Herzerkrankungen und Augenerkrankungen erkennen. Diese Technologie beschleunigt Diagnosen, reduziert menschliche Fehler und hilft, den weltweiten Fachkräftemangel in unterversorgten Regionen zu bewältigen. In einigen eng definierten Aufgaben und Studien haben Deep-Learning-Modelle eine vergleichbare Leistung wie Ärzt:innen gezeigt. Die Effektivität in der Praxis hängt von Validierung, Workflow-Integration und klinischer Überwachung ab. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ENatural Language Processing und Chatbots\u003C/h3\u003E\r\n\u003Cp\u003EDeep Learning unterstützt dialogorientierte KI-Systeme, die menschliche Sprache verstehen. So können Chatbots ohne menschliches Zutun Kundensupport bieten, Fragen beantworten und Transaktionen abschließen. Durch das Lernen aus großen Mengen an Text- und Gesprächsdaten sind diese Bots zunehmend in der Lage, komplexe Anfragen zu bearbeiten und natürliche, hilfreiche Antworten zu geben.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAutonome Fahrzeuge und Roboter\u003C/h3\u003E\r\n\u003Cp\u003ESelbstfahrende Autos und Roboter nutzen Deep Learning, um Kamerafeeds, Lidar-Daten und Sensorströme zu verarbeiten. So können sie ihre Umgebung verstehen, Hindernisse erkennen und Navigationsentscheidungen in Echtzeit treffen. Die Fähigkeit, die Welt um sie herum wahrzunehmen, ermöglicht es autonomen Systemen, sich an veränderte Straßenverhältnisse, Wetter und menschliches Verhalten anzupassen.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESpracherkennung und Audioverarbeitung\u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Modelle wandeln gesprochene Worte mit bemerkenswerter Genauigkeit in Text um und unterstützen Sprachassistenten wie Siri und Alexa sowie Barrierefreiheitstools für Menschen mit Hörbehinderungen. Diese Systeme lernen, mit verschiedenen Akzenten, Hintergrundgeräuschen und Sprachmustern umzugehen, wodurch Sprachinteraktionen zu einer praktischen Schnittstelle für eine Vielzahl von Geräten und Diensten werden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-58e73226dc","additionalClasses":"headline-decoration","type":"heading2","lines":["Wie funktioniert Deep Learning?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d849c81f41","text":"\u003Cp\u003EDeep-Learning-Modelle werden mit komplexen Netzwerken erstellt, die aus Tausenden künstlicher Neuronen bestehen – mathematische Operationen, die automatisch Muster aus gekennzeichneten Beispielen lernen und Millionen interner Einstellungen durch Ausprobieren anpassen, bis sie neue Daten, die sie bisher nicht gesehen haben, genau vorhersagen oder erkennen können.\u003C/p\u003E\r\n\u003Cp\u003EJedes Netzwerk besteht aus drei grundlegenden Teilen: einer Eingabeebene, in der markierte Daten erfasst werden, mehreren verborgenen Schichten von Neuronen, die die Daten analysieren und mit jedem Durchgang weiter verfeinern, und einer Ausgabeebene, in der die endgültige Vorhersage präsentiert wird. \u003C/p\u003E\r\n\u003Cp\u003ENehmen wir an, Sie möchten ein \u003Ca href=\"https://www.snowflake.com/de/fundamentals/neural-network/\"\u003Eneuronales Netzwerk\u003C/a\u003E darin trainieren, zu erkennen, ob ein Foto ein Bild eines Hundes oder einer Katze enthält. Sie beginnen damit, es mit Tausenden von Bildern zu füttern, die mit dem Label „Hund“ oder „Katze“ versehen sind, und lassen das Netzwerk die Unterschiede zwischen ihnen selbst herausfinden.\u003C/p\u003E\r\n\u003Cp\u003EDie erste versteckte Ebene könnte lernen, einfache Muster wie Kanten und Ecken zu erkennen. Die zweite versteckte Ebene kombiniert diese Kanten zu Formen wie Kreisen und Linien. Eine dritte Ebene kann Komponenten wie "spitze Ohren" oder "feuchte Nase" usw. erkennen. Mit jeder Ebene entwickelt das Netzwerk ein ausgefeilteres Verständnis, das von rohen Pixeln zu sinnvollen Konzepten übergeht.\u003C/p\u003E\r\n\u003Cp\u003EDie letzte Ebene enthält die Vorhersage des Netzwerks: ein Wahrscheinlichkeitswert, der angibt, ob das Bild einen Hund oder eine Katze zeigt. Wenn sich das Netzwerk irrt – z. B. die Vorhersage stimmt nicht mit dem Label überein, das den ursprünglichen Daten zugewiesen wurde –, unternimmt es automatisch einen neuen Versuch, wodurch einige Features des Bildes mehr Gewicht erhalten und andere weniger. Anschließend wiederholt es diesen Vorgang, bis es anhand von zurückgehaltenen Testdaten, je nach Qualität und Vielfalt der Trainingsdaten und Modelldesign, mit hoher Genauigkeit zwischen Hund und Katze unterscheiden kann. \u003C/p\u003E\r\n\u003Cp\u003EEin neuronales Netz lernt aus seinen Fehlern mithilfe eines Prozesses namens Backpropagation und arbeitet sich rückwärts durch die Ebenen, bis es herausfindet, welche Features am meisten zu der ungenauen Vorhersage beigetragen haben. Eine mathematische Formel, die als Datenverlustfunktion bezeichnet wird, sagt ihm dann, wie viel es korrigieren muss, wenn es etwas falsch macht. Wenn ein Modell die Markierung um ein Vielfaches verfehlt – sagen wir mit einer 95%igen Sicherheit vorhersagt, dass ein Foto einer Katze wirklich ein Hund ist –, untersucht es die Merkmale, die die Prognose in die falsche Richtung getrieben haben, und erhöht oder verringert das Gewicht, das es ihnen zuweist. Wenn es nur knapp verfehlt (das Modell ist nur zu 51 % sicher, dass es sich um ein Bild eines Hundes handelt) wird es diese Gewichte weniger drastisch verändern.\u003C/p\u003E\r\n\u003Cp\u003EDeshalb ist Deep Learning so leistungsstark geworden: Sobald Sie diesen Trainingsprozess eingerichtet haben, werden automatisch nützliche Funktionen und Darstellungen gefunden, ohne dass Nutzende sie selbst erstellen muss. Das Netzwerk lernt, worauf es ankommt. Und während Sie ihm mehr Daten und Rechenleistung zur Verfügung stellen, kann das Netzwerk immer komplexere Muster lernen und die Grenzen dessen überschreiten, was künstliche Intelligenz leisten kann.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f97c754dd4","additionalClasses":"headline-decoration","type":"heading2","lines":["Arten von Deep-Learning-Modellen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ef34c74f25","text":"\u003Cp\u003EEs gibt ungefähr ein halbes Dutzend verschiedener Deep-Learning-Architekturen, die jeweils auf bestimmte Datentypen und Aufgaben ausgerichtet sind. Hier sind die wichtigsten.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EConvolutional Neural Networks (CNNs)\u003C/h3\u003E\r\n\u003Cp\u003ECNNs wurden speziell entwickelt, um rasterartige Daten wie Bilder zu verarbeiten, indem nach Mustern wie Kanten, Texturen und Formen gesucht wird. Da CNNs verstehen, wie Pixel in der Nähe zusammenhängen, eignen sie sich hervorragend für Computer Vision-Aufgaben wie Bildklassifizierung, Objekterkennung, Gesichtserkennung und medizinische Bildanalyse. Damit sind sie äußerst effektiv für den Bau von Smartphone-Foto-Apps, die Gesichter erkennen, bis hin zu autonomen Fahrzeugen, die Fußgänger und Verkehrszeichen erkennen.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERekurrente neuronale Netze (RNNs)\u003C/h3\u003E\r\n\u003Cp\u003ERNNs wurden für Aufgaben entwickelt, bei denen es darauf ankommt, die Reihenfolge der Daten beizubehalten, wie z. B. die Analyse von Sätzen in einem Dokument oder von Frames in einem Video. Da RNNs neue Daten verarbeiten und sich gleichzeitig an die Daten erinnern können, die sie gerade analysiert haben, sind sie nützlich für Sprachübersetzungen, Spracherkennung und Zeitreihenvorhersage. Neuere Transformer-Netzwerke haben sie für viele Sprachaufgaben weitgehend ersetzt. Doch RNNs bleiben auch dann wertvoll, wenn sie mit kontinuierlichen Datenströmen wie Echtzeit-Sensormessungen arbeiten oder wenn die Rechenressourcen begrenzt sind.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGenerative Adversarial Networks (GANs)\u003C/h3\u003E\r\n\u003Cp\u003EGANs bestehen aus zwei neuronalen Netzen, die miteinander konkurrieren: einem Generator, der synthetische Daten (wie gefälschte Bilder) erzeugt, und einem Diskriminator, der versucht, echte Daten von gefälschten zu unterscheiden. Durch diesen widersprüchlichen Trainingsprozess wird der Generator immer fähiger, realistische Ausgaben zu produzieren, wodurch GANs leistungsstark für die Erstellung fotorealistischer Bilder, die Generierung synthetischer Trainingsdaten und sogar die Erzeugung von Deepfakes werden. Sie wurden verwendet, um Kunstwerke zu erstellen, Bilder mit niedriger Auflösung zu verbessern, realistische Gesichter von Menschen zu erzeugen, die nicht existieren, und neue Moleküle für die Arzneimittelforschung zu entwickeln.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ETransformer-Netzwerke\u003C/h3\u003E\r\n\u003Cp\u003ETransformer revolutionierten die Verarbeitung natürlicher Sprache, indem sie einen „Aufmerksamkeitsmechanismus“ einsetzten, der es dem Netzwerk ermöglicht, sich auf die wichtigsten Teile der Eingabe gleichzeitig zu konzentrieren, anstatt Daten sequentiell zu verarbeiten. Diese Architektur ermöglicht es modernen \u003Ca href=\"https://www.snowflake.com/de/fundamentals/large-language-model/\"\u003ELarge Language Models\u003C/a\u003E wie GPT und Claude, Kontexte über lange Textabschnitte hinweg zu verstehen, menschenähnliche Schriften zu generieren und Aufgaben wie Übersetzung und Zusammenfassung mit nie dagewesener Genauigkeit auszuführen. Transformer haben sich auch über Sprache hinaus als effektiv erwiesen: Jüngste Anpassungen haben eine starke Performance beim Computer Vision gezeigt und sogar die Struktur von Proteinen vorhergesagt.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAutoencoder\u003C/h3\u003E\r\n\u003Cp\u003EAutoencoder arbeiten, indem sie Daten auf ihre wichtigsten Merkmale komprimieren und sie dann aus dieser komprimierten Form wiederherstellen. Das macht sie nützlich, um ungewöhnliche Muster zu erkennen (alles, was sich nicht gut rekonstruieren lässt, ist wahrscheinlich ungewöhnlich), verrauschte Daten zu bereinigen und komplexe Datasets auf ihre Kernelemente zu reduzieren. Dank der Fähigkeit, Anomalien in den Daten schnell zu erkennen, sind Autoencoder nützlich, um betrügerische Kredittransaktionen zu erkennen oder Produktmängel an Fließbändern zu erkennen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-a88c57aa9b","additionalClasses":"headline-decoration","type":"heading2","lines":["Wichtige Unterschiede zwischen maschinellem Lernen, Deep Learning und generativer KI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-135b83c81a","text":"\u003Cp\u003EDrei verwandte, aber unterschiedliche KI-Paradigmen beherrschen heute die Entwicklung von KI-Modellen. Hier sind die größten Unterschiede unter ihnen. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMaschinelles Lernen\u003C/h3\u003E\n\u003Cp\u003EModelle für maschinelles Lernen verwenden Algorithmen, die Muster aus Daten lernen. In der Regel müssen Menschen jedoch relevante Merkmale manuell entwerfen und extrahieren, bevor der Algorithmus daraus lernen kann. Diese Systeme arbeiten gut mit strukturierten, tabellarischen Daten und relativ bescheidenen Datasets und eignen sich daher für Anwendungen wie Credit Scoring, Kundensegmentierung und einfache Empfehlungssysteme. Modelle für maschinelles Lernen sind in der Regel einfacher zu interpretieren als Deep-Learning-Modelle und erfordern weniger Rechenleistung für Training und Bereitstellung.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDeep Learning\u003C/h3\u003E\n\u003Cp\u003EDeep Learning nutzt mehrschichtige neuronale Netze, die automatisch erkennen, welche Features wichtig sind, wodurch manuelles Feature Engineering, wie es beim herkömmlichen maschinellen Lernen erforderlich ist, überflüssig wird. Diese Systeme brillieren mit unstrukturierten Daten wie Bildern, Audio und Text, erfordern aber große Trainings-Datasets (oft Millionen von Beispielen) und erhebliche Rechenressourcen, um effektiv zu lernen. Deep Learning unterstützt Anwendungen, die das Verständnis komplexer Muster erfordern, wie Gesichtserkennung, autonome Fahrzeuge, medizinische Bilddiagnose und Spracherkennungssysteme.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGenerative KI\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EGenerative KI\u003C/a\u003E ist eine Untergruppe von Deep Learning. Doch anstatt die Ausgaben bestehender Daten zu klassifizieren oder vorherzusagen, sind sie speziell darauf ausgelegt, neue Inhalte zu erstellen, darunter Texte, Bilder, Musik, Code oder Videos. Um diese Systeme zu trainieren, braucht es wirklich riesige Datasets (oft Milliarden von Beispielen) mit Architekturen wie Transformern und GANs, die die zugrunde liegenden Muster und Strukturen der Trainingsdaten gut genug lernen, um neue, realistische Ergebnisse zu generieren. Generative KI ist die Grundlage für Anwendungen wie ChatGPT und Claude (dialogorientierte KI), DALL-E und Midjourney (Bildgenerierung), GitHub Copilot (Codevervollständigung) und Systeme, die synthetische Trainingsdaten oder personalisierte Inhalte in großem Umfang erstellen.\u003C/p\u003E\n\u003Cp\u003ENeben diesen dreien sind noch eine Handvoll weiterer KI-Paradigmen erwähnenswert. Klassische (oder symbolische) KI nutzt explizite Regeln, Logik und Wissen, die von Menschen programmiert werden. Dieses Paradigma wird von Expertensystemen und regelbasierten Chatbots verwendet. Im Rahmen des Reinforcement Learning interagieren KI-Agenten mit ihrer Umgebung und erhalten Belohnungen oder Strafen, je nachdem, welche Maßnahmen sie ergreifen. Dieses Modell wird häufig in Robotersteuerungssystemen und Empfehlungs-Engines eingesetzt, die von Benutzerinteraktionen lernen. Evolutionäre Algorithmen sind von der biologischen Evolution inspiriert und ermöglichen es Modellen, sich im Laufe der Zeit kontinuierlich zu verbessern und fitter zu werden. Sie werden eingesetzt, um Probleme wie das Design neuronaler Netze oder die Optimierung der Lieferkette zu lösen. Neurosymbolische KI kombiniert neuronale Netze (Lernen aus Daten) mit symbolischem Denken (logische Regeln und Wissen). Dieses neue Paradigma steht erst am Anfang realer Anwendungen zur Verbesserung medizinischer Diagnosen und zur Verbesserung der Cybersicherheit.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2da7dbc474","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile von Deep-Learning-Modellen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b254fc6771","text":"\u003Cp\u003EDeep-Learning-Algorithmen genießen gegenüber anderen KI-Paradigmen eine Reihe von Vorteilen. Hier einige ihrer größten Stärken.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie sind hochpräzise bei komplexen Aufgaben\u003C/h3\u003E\r\n\u003Cp\u003EDeep Learning kann bei bestimmten komplexen Aufgaben (z. B. Bildklassifizierung und Spracherkennung) modernste Performance erzielen, je nach Modell, Daten und Evaluierungs-Setup. Modelle können subtile Merkmale und Beziehungen in Daten erkennen, die für Menschen nahezu unmöglich explizit zu identifizieren oder zu programmieren wären, wie z. B. das Erkennen von ersten Krankheitsanzeichen in medizinischen Scans oder die Vorhersage von Proteinstrukturen. Dieser Genauigkeitsvorteil wird noch deutlicher, je komplexer Aufgaben werden, wodurch Deep Learning zum bevorzugten Ansatz für Probleme wird, an denen herkömmliche Methoden in der Vergangenheit gescheitert sind.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie erkennen automatisch relevante Datenmerkmale \u003C/h3\u003E\r\n\u003Cp\u003EIm Gegensatz zum herkömmlichen maschinellen Lernen findet Deep Learning automatisch heraus, welche Features wichtig sind, ohne dass Domänenexpert:innen sie manuell entwerfen und extrahieren müssen. Das Netzwerk lernt hierarchische Darstellungen von selbst: Es identifiziert Kanten in ersten Ebenen, kombiniert sie in Formen in mittleren Ebenen und erkennt übergeordnete Konzepte in späteren Ebenen. Diese Automatisierung verkürzt die Entwicklungszeit drastisch und ermöglicht Deep Learning, Probleme in Bereichen anzugehen, in denen menschliche Fachleute möglicherweise nicht einmal wissen, welche Funktionen relevant sind.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie können problemlos über große Datasets hinweg skaliert werden\u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Modelle verbessern sich vorhersehbar, je mehr Trainingsdaten Sie zur Verfügung stellen, während herkömmliche Algorithmen für maschinelles Lernen oft nach einem bestimmten Punkt ein Plateau erreichen. Dank dieser Skalierbarkeit können Unternehmen mit Zugriff auf riesige Datasets eine deutlich bessere Performance erzielen, indem sie in mehr Datenerfassung und größere Modelle investieren. Das Verhältnis zwischen Datenvolumen und Performance schafft einen zusätzlichen Vorteil für Unternehmen, die Informationen in großem Umfang erfassen und verarbeiten können.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie können Entscheidungen in Echtzeit treffen \u003C/h3\u003E\r\n\u003Cp\u003EEinmal trainiert, können Deep-Learning-Modelle Informationen extrem schnell verarbeiten und Prognosen treffen und so Echtzeitanwendungen ermöglichen, die sofortige Reaktionen erfordern. Dank dieser Geschwindigkeit eignet sich Deep Learning für autonome Fahrzeuge, die Hindernisse erkennen und sofort reagieren müssen, Betrugserkennungssysteme, die Transaktionen direkt nach ihrem Auftreten auswerten, und Sprachassistenten, die ohne spürbare Verzögerung auf gesprochene Befehle reagieren. Moderne Hardwareoptimierungen und Modellkomprimierungstechniken verbessern die Inferenzgeschwindigkeit und erweitern so das Angebot an Echtzeitanwendungen.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie können unstrukturierte Daten hervorragend verarbeiten\u003C/h3\u003E\r\n\u003Cp\u003EDeep Learning eignet sich hervorragend für die Verarbeitung unstrukturierter Datentypen, die keine klare tabellarische Organisation aufweisen, wie z. B. Bilder, Video, Audio, Text und Sensorströme, mit denen herkömmliche Algorithmen zu kämpfen haben. Diese Funktion schöpft einen Mehrwert aus dem enormen Volumen an E-Mails, Kundenservice-Aufzeichnungen, Überwachungskamera-Filmmaterial und Social-Media-Beiträgen, die Unternehmen generieren. Deep Learning ermöglicht völlig neue Anwendungskategorien und Einblicke, indem es bisher unbrauchbare Daten für Analysen zugänglich macht.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie passen sich schnell an neue Aufgaben an\u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Modelle, die mit einer Aufgabe trainiert wurden, können oft mit minimalem zusätzlichen Training an verwandte Aufgaben angepasst werden, wodurch der Daten- und Zeitaufwand für neue Anwendungen drastisch reduziert wird. Ein Modell, das auf die Erkennung von Alltagsgegenständen trainiert wurde, kann beispielsweise so optimiert werden, dass es bestimmte Krankheiten erkennt – mit deutlich weniger medizinischen Bildern, als es das Training von Grund auf erfordern würde. Diese als Transfer-Learning bekannte Technik ermöglicht es Unternehmen, bestehende Modelle als Ausgangspunkt zu nutzen, Entwicklungszyklen zu beschleunigen und Deep Learning zugänglicher zu machen, selbst wenn domänenspezifische Daten begrenzt sind.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESie lernen immer dazu \u003C/h3\u003E\r\n\u003Cp\u003EDeep-Learning-Systeme können kontinuierlich mit neuen Daten aktualisiert werden. So können sie sich an veränderte Muster anpassen, die Genauigkeit im Laufe der Zeit verbessern und neue Bedingungen bewältigen, ohne komplett umzuschulen. Diese Lernfähigkeit bedeutet, dass Modelle, die in der Produktion eingesetzt werden, besser werden können, da sie auf realere Beispiele treffen und sich natürlich an verändertes Nutzerverhalten, Marktbedingungen oder Umweltfaktoren anpassen. Die schrittweise Verbesserung macht Deep-Learning-Systeme im Vergleich zu statischen regelbasierten Systemen robuster und nachhaltiger für die langfristige Bereitstellung.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-8d99ee6af9","additionalClasses":"headline-decoration","type":"heading2","lines":["Nachteile von Deep-Learning-Modellen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-be1c35838a","text":"\u003Cp\u003EDeep-Learning-Modelle sind zwar für verschiedenste Anwendungsfälle äußerst nützlich, bringen aber auch enorme Herausforderungen mit sich, was ihre Kosten, ihren Energieverbrauch, ihre Interpretationsfähigkeit und ihr Missbrauchspotenzial angeht. Hier sind die größten Nachteile von Deep Learning.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESie brauchen viel Rechenleistung \u003C/h3\u003E\n\u003Cp\u003EDas Training von Deep-Learning-Modellen erfordert erhebliche Rechenleistung, die oft mit teurer spezialisierter Hardware wie GPUs über Tage oder Wochen läuft. Ihr Energieverbrauch kann enorm sein: Das Training großer Modelle kann energieintensiv sein, wobei die Anforderungen je nach Modellgröße, Hardware und Trainingsdauer stark variieren. Die Bereitstellung von Modellen für Echtzeit-Inferenzen in großem Umfang erfordert auch laufende Rechenressourcen und Infrastrukturinvestitionen, was Deep Learning für einige Anwendungen und kleinere Unternehmen wirtschaftlich unpraktikabel macht.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESie erfordern große Mengen an gekennzeichneten Daten\u003C/h3\u003E\n\u003Cp\u003EDeep-Learning-Modelle benötigen in der Regel Tausende bis Millionen von gekennzeichneten Trainingsbeispielen, um eine gute Leistung zu erzielen. Und diese Labels zu erstellen, erfordert oft erheblichen menschlichen Aufwand und Fachwissen. In spezialisierten Bereichen wie der medizinischen Bildgebung oder der Diagnose seltener Krankheiten, in denen Expert:innen jedes Beispiel manuell überprüfen und annotieren müssen, kann es extrem schwierig oder teuer sein, ausreichend gekennzeichnete Daten zu erhalten. Diese Datenanforderung schafft ein Kaltstartproblem, bei dem Deep Learning nicht effektiv angewendet werden kann, ohne dass zuvor massiv in die Datenerfassung und -kennzeichnung investiert wird. Dadurch sind fortschrittliche Anwendungen für Unternehmen ohne wesentliche Datenressourcen außer Reichweite.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESie können anfällig für Überanpassung sein\u003C/h3\u003E\n\u003Cp\u003EDeep-Learning-Modelle können Trainingsdaten auswendig lernen, anstatt zu lernen, wie man Muster innerhalb dieser Daten erkennt. Ein Overfit-Modell schneidet bei Trainingsbeispielen extrem gut ab, scheitert aber, wenn es in neue, leicht abweichende Situationen stößt – wie ein Gesichtserkennungssystem, das im Labor perfekt funktioniert, aber in der Produktion mit unterschiedlichen Lichtverhältnissen oder Kamerawinkeln zu kämpfen hat. Um eine Überanpassung zu verhindern, sind Techniken wie Regularisierung, Abbruch und Validierungstests erforderlich. Doch selbst mit diesen Sicherheitsmaßnahmen können Modelle immer noch Scheinkorrelationen lernen, die in der realen Welt nicht Bestand haben.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIhre Operationen sind undurchsichtig \u003C/h3\u003E\n\u003Cp\u003EOft ist es unmöglich, genau zu verstehen, warum ein Deep-Learning-Modell eine bestimmte Prognose getroffen hat. Das macht sie für Anwendungen problematisch, bei denen Erklärungen rechtlich oder ethisch notwendig sind. Ein Deep-Learning-basiertes Kreditgenehmigungssystem kann Antragsteller beispielsweise ablehnen, ohne dass sie in der Lage sind zu erklären, welche Faktoren für diese Entscheidung verantwortlich sind. So könnte es gegen Gesetze zur fairen Kreditvergabe verstoßen oder versteckte Vorurteile verbergen. Dieses „Blackbox-Problem“ bringt in regulierten Branchen wie Gesundheitswesen und Finanzen Herausforderungen mit sich. Dies erschwert auch das Debuggen von Modellen, wenn sie fehlschlagen, oder die Überprüfung, ob sie Entscheidungen aus den richtigen Gründen treffen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESie werfen starke ethische Bedenken auf\u003C/h3\u003E\n\u003Cp\u003EDa Deep-Learning-Modelle aus historischen Daten lernen, absorbieren und verstärken sie unweigerlich jeden Bias, der in diesen Daten vorhanden ist. Das könnte die Diskriminierung in Einstellungs-, Kredit-, Strafverfolgungs- und anderen sensiblen Bereichen fortsetzen. Ein Gesichtserkennungssystem, das vor allem mit hellhäutigen Gesichtern trainiert wird, wird bei dunkelhäutigen Menschen schlecht abschneiden, und ein Lebenslauf-Screening-Tool, das mit historischen Einstellungsentscheidungen trainiert wurde, kann Frauen oder Minderheiten diskriminieren. 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