{"id":40426,"date":"2023-06-21T17:40:14","date_gmt":"2023-06-21T17:40:14","guid":{"rendered":"https:\/\/www.cyberguru.it\/erklaerbare-ki-mit-python\/"},"modified":"2024-10-14T09:53:45","modified_gmt":"2024-10-14T09:53:45","slug":"erklaerbare-ki-mit-python","status":"publish","type":"page","link":"https:\/\/www.cyberguru.it\/de\/erklaerbare-ki-mit-python\/","title":{"rendered":"Erkl\u00e4rbare KI mit Python"},"content":{"rendered":"

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Erkl\u00e4rbare KI mit Python<\/span><\/h1>\n

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Von Leonida Gianfagna und Antonio Di Cecco<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=“1″ _builder_version=“4.21.0″ _module_preset=“default“ background_image=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2022\/10\/Rete_Neuronale-02-2.webp“ global_colors_info=“{}“][et_pb_row column_structure=“1_2,1_2″ use_custom_gutter=“on“ make_equal=“on“ custom_padding_last_edited=“off|desktop“ _builder_version=“4.19.4″ _module_preset=“default“ width=“100%“ max_width=“2560px“ custom_margin_tablet=““ custom_margin_phone=“||||false|true“ custom_margin_last_edited=“on|phone“ custom_padding=“|0.2%||0px|false|false“ custom_padding_tablet=“|50px||50px|false|true“ custom_padding_phone=“|30px||30px|false|true“ global_colors_info=“{}“][et_pb_column type=“1_2″ _builder_version=“4.19.4″ _module_preset=“default“ custom_css_main_element=“align-self:center;“ global_colors_info=“{}“][et_pb_image src=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2023\/06\/ebook4.png“ alt=“Ebook Leonidas“ title_text=“Ebook Leonidas“ _builder_version=“4.21.0″ _module_preset=“default“ global_colors_info=“{}“][\/et_pb_image][\/et_pb_column][et_pb_column type=“1_2″ _builder_version=“4.19.4″ _module_preset=“default“ custom_padding=“|8%|||false|false“ custom_padding_tablet=“|8%|||false|false“ custom_padding_phone=“|5%||5%|false|true“ custom_padding_last_edited=“on|phone“ custom_css_main_element=“align-self:center;“ global_colors_info=“{}“][et_pb_blurb alt=“Cyberguru Favicon“ icon_placement=“left“ content_max_width=“1100px“ _builder_version=“4.21.0″ _module_preset=“default“ body_font=“Rubik||||||||“ body_text_color=“gcid-65b65f10-97af-4b49-9890-b01efe796309″ body_font_size=“18px“ module_alignment=“left“ custom_css_main_element=“font-weight:400;“ global_colors_info=“{%22gcid-65b65f10-97af-4b49-9890-b01efe796309%22:%91%22body_text_color%22%93}“]<\/p>\n

<\/span>Explainable AI with Python<\/strong> bietet eine umfassende Darstellung der Konzepte und Techniken, die heute verf\u00fcgbar sind, um Machine Learning Systeme verst\u00e4ndlicher zu machen. Die vorgestellten Ans\u00e4tze lassen sich auf fast alle aktuellen Modelle des maschinellen Lernens anwenden, wie z.B. lineare und logistische Regressionsmodelle, neuronale Netze f\u00fcr tiefes Lernen, nat\u00fcrliche Sprachverarbeitung und Bilderkennung. <\/span><\/p>\n

Die Fortschritte im Bereich des maschinellen Lernens tragen dazu bei, dass immer mehr k\u00fcnstliche Agenten eingesetzt werden, die kritische Aufgaben \u00fcbernehmen k\u00f6nnen, die bisher von Menschen erledigt wurden (z. B. im Gesundheitswesen oder im Rechts- und Finanzwesen). Obwohl die Prinzipien f\u00fcr die Entwicklung dieser Agenten klar sind, bleiben die meisten der verwendeten Deep-Learning-Modelle f\u00fcr den Menschen „undurchsichtig“. Das Buch f\u00fcllt die derzeitige L\u00fccke in der Literatur zu diesem Thema, indem es sowohl eine theoretische als auch eine praktische Perspektive einnimmt und den Leser schnell in die Lage versetzt, mit den Tools und dem Code zu arbeiten, die f\u00fcr erkl\u00e4rbare KI-Systeme verwendet werden. <\/span><\/span><\/p>\n

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Das Buch beginnt mit Beispielen daf\u00fcr, was erkl\u00e4rbare KI (Explainable AI, XAI) ist und warum sie so notwendig ist. Anschlie\u00dfend werden verschiedene Ans\u00e4tze der XAI je nach Kontext und spezifischen Bed\u00fcrfnissen erl\u00e4utert. Anschlie\u00dfend werden praktische Beispiele f\u00fcr interpretierbare Modelle unter Verwendung von Python vorgestellt, die zeigen, wie inh\u00e4rent interpretierbare Modelle interpretiert werden k\u00f6nnen und wie „menschlich verst\u00e4ndliche“ Erkl\u00e4rungen erstellt werden k\u00f6nnen. Es wird gezeigt, dass agnostische Modellmethoden f\u00fcr XAI Erkl\u00e4rungen liefern, die sich nicht auf die Interna von ML-Modellen st\u00fctzen, die „undurchsichtig“ sind. Anhand von Beispielen aus der Computer Vision untersuchen die Autoren dann erkl\u00e4rbare Modelle f\u00fcr Deep Learning und m\u00f6gliche Methoden f\u00fcr die Zukunft. Mit einer praktischen Perspektive zeigen die Autoren auch, wie man ML und XAI in der Wissenschaft effektiv einsetzen kann. Das letzte Kapitel erkl\u00e4rt Adversarial Machine Learning und wie man XAI anhand widerspr\u00fcchlicher Beispiele durchf\u00fchrt. <\/span><\/p>\n

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Wenn Sie an dem Buch Explainable AI with Python interessiert sind , klicken Sie hier.<\/a><\/strong><\/em><\/p>\n

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<\/h2>\n

Die Autoren<\/h2>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=“1″ fullwidth=“on“ _builder_version=“4.21.0″ _module_preset=“default“ global_colors_info=“{}“][\/et_pb_section][et_pb_section fb_built=“1″ _builder_version=“4.19.4″ _module_preset=“default“ min_height=“591.6px“ custom_padding=“0px||63px|||“ global_colors_info=“{}“][et_pb_row column_structure=“1_2,1_2″ use_custom_gutter=“on“ gutter_width=“2″ make_equal=“on“ _builder_version=“4.21.0″ _module_preset=“default“ max_width=“1440px“ custom_margin=“15px|auto||auto||“ global_colors_info=“{}“][et_pb_column type=“1_2″ _builder_version=“4.18.0″ _module_preset=“default“ background_color=“#FFFFFF“ background_image=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2022\/10\/Awareness-tips.webp“ custom_padding=“20px|30px|20px|30px|true|true“ border_radii=“on|20px|20px|20px|20px“ box_shadow_style=“preset1″ box_shadow_blur=“13px“ global_colors_info=“{}“][et_pb_image src=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2023\/06\/profile-07.png“ alt=“Diplom“ title_text=“Profil-07″ align=“center“ _builder_version=“4.21.0″ _module_preset=“default“ width=“30%“ width_tablet=“30%“ width_phone=“30%“ width_last_edited=“on|phone“ global_colors_info=“{}“][\/et_pb_image][et_pb_text content_tablet=“<\/p>\n

KARTE<\/p>\n

“ content_phone=“Leonida Gianfagna“ content_last_edited=“on|desktop“ _builder_version=“4.21.0″ _module_preset=“default“ text_font=“Inter|600|||||||“ text_text_color=“#000000″ text_font_size=“18px“ text_orientation=“center“ custom_padding=“20px|0px|20px|0px|false|true“ custom_padding_tablet=“20px||20px|40px|false|false“ custom_padding_phone=“20px|0px|20px|0px|false|true“ custom_padding_last_edited=“on|phone“ hover_enabled=“0″ locked=“off“ global_colors_info=“{}“ sticky_enabled=“0″]<\/p>\n

Leonida Gianfagna<\/strong><\/h2>\n

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Leonida Gianfagna (PhD, MBA) ist theoretischer Physiker und arbeitet derzeit im Bereich Cybersicherheit als F&E-Direktor bei Cyber Guru. Bevor er zu Cyber Guru kam, arbeitete er 15 Jahre lang bei IBM in leitenden Positionen in der Softwareentwicklung im Bereich ITSM (IT Service Management). Er hat mehrere Publikationen in theoretischer Physik und Informatik verfasst und ist ein akkreditierter IBM Master Inventor. <\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=“1_2″ _builder_version=“4.18.0″ _module_preset=“default“ background_color=“#FFFFFF“ background_image=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2022\/10\/Awareness-tips.webp“ custom_padding=“20px|30px|20px|30px|true|true“ border_radii=“on|20px|20px|20px|20px“ box_shadow_style=“preset1″ box_shadow_blur=“13px“ global_colors_info=“{}“][et_pb_image src=“https:\/\/www.cyberguru.it\/wp-content\/uploads\/2023\/06\/profile-08.png“ alt=“Dialog“ title_text=“Profil-08″ align=“center“ _builder_version=“4.21.0″ _module_preset=“default“ width=“30%“ width_tablet=“30%“ width_phone=“30%“ width_last_edited=“on|phone“ global_colors_info=“{}“][\/et_pb_image][et_pb_text content_tablet=“<\/p>\n

KARTE<\/p>\n

“ content_phone=“Antonio Di Cecco“ content_last_edited=“on|desktop“ _builder_version=“4.21.0″ _module_preset=“default“ text_font=“Inter|600|||||||“ text_text_color=“#000000″ text_font_size=“18px“ text_orientation=“center“ width=“100%“ custom_padding=“20px|0px|20px|0px|false|true“ custom_padding_tablet=“20px||20px|40px|false|false“ custom_padding_phone=“20px|0px|20px|0px|false|true“ custom_padding_last_edited=“on|phone“ hover_enabled=“0″ locked=“off“ global_colors_info=“{}“ sticky_enabled=“0″]<\/p>\n

Antonio Di Cecco<\/strong><\/h2>\n

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Antonio Di Cecco ist ein theoretischer Physiker mit einem soliden mathematischen Hintergrund, der aktiv an der AIML-Ausbildung beteiligt ist. Die St\u00e4rke seines Ansatzes liegt in der eingehenden Untersuchung der mathematischen Grundlagen von AIML-Modellen, die neue Wege zur Darstellung von AIML-Wissen und Raum f\u00fcr Verbesserungen gegen\u00fcber dem bestehenden Stand der Technik bieten k\u00f6nnen. <\/span><\/p>\n

<\/span><\/p>\n

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M\u00f6chten Sie mehr erfahren?<\/h2>\n

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Erkl\u00e4rbare KI mit PythonVon Leonida Gianfagna und Antonio Di CeccoExplainable AI with Python bietet eine umfassende Darstellung der Konzepte und Techniken, die heute verf\u00fcgbar sind, um Machine Learning Systeme verst\u00e4ndlicher zu machen. Die vorgestellten Ans\u00e4tze lassen sich auf fast alle aktuellen Modelle des maschinellen Lernens anwenden, wie z.B. lineare und logistische Regressionsmodelle, neuronale Netze f\u00fcr […]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"class_list":["post-40426","page","type-page","status-publish","hentry"],"yoast_head":"\nErkl\u00e4rbare KI mit Python<\/title>\n<meta name=\"description\" content=\"Wie kann man Machine Learning Systeme verst\u00e4ndlicher machen? Explainable AI with Python von Gianfagna-Di Cecco gibt eine konkrete Antwort\" \/>\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.cyberguru.it\/de\/erklaerbare-ki-mit-python\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Erkl\u00e4rbare KI mit Python\" \/>\n<meta property=\"og:description\" content=\"Wie kann man Machine Learning Systeme verst\u00e4ndlicher machen? 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