{"id":31869,"date":"2023-06-21T17:40:14","date_gmt":"2023-06-21T17:40:14","guid":{"rendered":"https:\/\/www.cyberguru.it\/?page_id=31869"},"modified":"2024-03-10T14:08:24","modified_gmt":"2024-03-10T14:08:24","slug":"explainable-ai-with-python","status":"publish","type":"page","link":"https:\/\/www.cyberguru.it\/es\/explainable-ai-with-python\/","title":{"rendered":"Explainable AI with Python"},"content":{"rendered":"

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Explainable AI with Python<\/span><\/h1>\n

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De Leonida Gianfagna y Antonio Di Cecco<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\u00bb1″ _builder_version=\u00bb4.21.0″ _module_preset=\u00bbdefault\u00bb background_image=\u00bbhttps:\/\/www.cyberguru.it\/wp-content\/uploads\/2022\/10\/Rete_Neuronale-02-2.webp\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_row column_structure=\u00bb1_2,1_2″ use_custom_gutter=\u00bbon\u00bb make_equal=\u00bbon\u00bb custom_padding_last_edited=\u00bboff|desktop\u00bb _builder_version=\u00bb4.19.4″ _module_preset=\u00bbdefault\u00bb width=\u00bb100%\u00bb max_width=\u00bb2560px\u00bb custom_margin_tablet=\u00bb\u00bb custom_margin_phone=\u00bb||||false|true\u00bb custom_margin_last_edited=\u00bbon|phone\u00bb custom_padding=\u00bb|0.2%||0px|false|false\u00bb custom_padding_tablet=\u00bb|50px||50px|false|true\u00bb custom_padding_phone=\u00bb|30px||30px|false|true\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb1_2″ _builder_version=\u00bb4.19.4″ _module_preset=\u00bbdefault\u00bb custom_css_main_element=\u00bbalign-self:center;\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_image src=\u00bbhttps:\/\/www.cyberguru.it\/wp-content\/uploads\/2023\/06\/ebook4.png\u00bb alt=\u00bbEbook Leonida\u00bb title_text=\u00bbEbook Leonida\u00bb _builder_version=\u00bb4.21.0″ _module_preset=\u00bbdefault\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00bb1_2″ _builder_version=\u00bb4.19.4″ _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb|8%|||false|false\u00bb custom_padding_tablet=\u00bb|8%|||false|false\u00bb custom_padding_phone=\u00bb|5%||5%|false|true\u00bb custom_padding_last_edited=\u00bbon|phone\u00bb custom_css_main_element=\u00bbalign-self:center;\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_blurb alt=\u00bbCyberguru favicon\u00bb icon_placement=\u00bbleft\u00bb content_max_width=\u00bb1100px\u00bb _builder_version=\u00bb4.21.0″ _module_preset=\u00bbdefault\u00bb body_font=\u00bbRubik||||||||\u00bb body_text_color=\u00bbgcid-65b65f10-97af-4b49-9890-b01efe796309″ body_font_size=\u00bb18px\u00bb module_alignment=\u00bbleft\u00bb custom_css_main_element=\u00bbfont-weight:400;\u00bb global_colors_info=\u00bb{%22gcid-65b65f10-97af-4b49-9890-b01efe796309%22:%91%22body_text_color%22%93}\u00bb]<\/p>\n

\u00abExplainable AI with Python\u00bb<\/strong> <\/span>ofrece una presentaci\u00f3n exhaustiva de los conceptos y t\u00e9cnicas disponibles en la actualidad para hacer m\u00e1s comprensibles los sistemas de aprendizaje autom\u00e1tico. Los enfoques presentados pueden aplicarse a casi todos los modelos actuales de aprendizaje autom\u00e1tico, como los modelos de regresi\u00f3n lineal y log\u00edstica, las redes neuronales de aprendizaje profundo, el procesamiento del lenguaje natural y el reconocimiento de im\u00e1genes.<\/span><\/p>\n

Los avances en el aprendizaje autom\u00e1tico est\u00e1n contribuyendo a aumentar el uso de agentes artificiales capaces de realizar tareas cr\u00edticas de las que antes se encargaban los humanos (como en la asistencia sanitaria o en las actividades jur\u00eddicas y financieras). Aunque se conocen los principios que gu\u00edan el dise\u00f1o de estos agentes, la mayor\u00eda de los modelos actuales de aprendizaje profundo resultan \u00abopacos\u00bb a la comprensi\u00f3n humana. El libro llena el vac\u00edo actual existente en la documentaci\u00f3n que trata este tema, al adoptar una perspectiva tanto te\u00f3rica como pr\u00e1ctica y capacitar r\u00e1pidamente al lector para trabajar con las herramientas y el c\u00f3digo utilizados en los sistemas de Explainable AI.<\/span><\/span><\/p>\n

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Tras comenzar con ejemplos sobre qu\u00e9 es la Explainable AI (XAI) y por qu\u00e9 es tan necesaria, el libro detalla distintos enfoques de la XAI en funci\u00f3n del contexto y las necesidades espec\u00edficas. A continuaci\u00f3n, se presentan ejemplos pr\u00e1cticos de modelos interpretables utilizando Python, que muestran c\u00f3mo pueden interpretarse los modelos intr\u00ednsecamente interpretables y c\u00f3mo pueden producirse explicaciones \u00abcomprensibles para el ser humano\u00bb. Se demuestra que los m\u00e9todos de modelos agn\u00f3sticos para XAI producen explicaciones que no dependen de las partes internas de los modelos ML, que son \u00abopacos\u00bb. Mediante el uso de ejemplos de Computer Vision, los autores examinan a continuaci\u00f3n los modelos explicables para el aprendizaje profundo y posibles m\u00e9todos para el futuro. Gracias a una perspectiva pr\u00e1ctica, los autores tambi\u00e9n demuestran c\u00f3mo utilizar eficazmente en la ciencia el aprendizaje autom\u00e1tico y la XAI. El \u00faltimo cap\u00edtulo explica el aprendizaje autom\u00e1tico adverso y c\u00f3mo crear XAI utilizando ejemplos conflictivos.<\/span><\/p>\n

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Si te interesa el libro \u00abExplainable AI with Python\u00bb, haz clic aqu\u00ed.<\/a><\/strong><\/em><\/p>\n

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<\/h2>\n

Los autores<\/h2>\n

 <\/p>\n

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CARD<\/p>\n

\u00bb content_phone=\u00bbLeonida Gianfagna\u00bb content_last_edited=\u00bbon|desktop\u00bb _builder_version=\u00bb4.21.0″ _module_preset=\u00bbdefault\u00bb text_font=\u00bbInter|600|||||||\u00bb text_text_color=\u00bb#000000″ text_font_size=\u00bb18px\u00bb text_orientation=\u00bbcenter\u00bb custom_padding=\u00bb20px|0px|20px|0px|false|true\u00bb custom_padding_tablet=\u00bb20px||20px|40px|false|false\u00bb custom_padding_phone=\u00bb20px|0px|20px|0px|false|true\u00bb custom_padding_last_edited=\u00bbon|phone\u00bb hover_enabled=\u00bb0″ locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb sticky_enabled=\u00bb0″]<\/p>\n

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

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Leonida Gianfagna (PhD, MBA) es f\u00edsico te\u00f3rico y actualmente trabaja en ciberseguridad como director de I+D para Cyber Guru. Antes de incorporarse a Cyber Guru, trabaj\u00f3 en IBM durante 15 a\u00f1os en funciones directivas de desarrollo de \u00absoftware\u00bb en ITSM (Gesti\u00f3n de Servicios de TI). Es autor de varias publicaciones sobre f\u00edsica te\u00f3rica e inform\u00e1tica y est\u00e1 acreditado como IBM Master Inventor.<\/span><\/p>\n

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CARD<\/p>\n

\u00bb content_phone=\u00bbAntonio Di Cecco\u00bb content_last_edited=\u00bbon|desktop\u00bb _builder_version=\u00bb4.21.0″ _module_preset=\u00bbdefault\u00bb text_font=\u00bbInter|600|||||||\u00bb text_text_color=\u00bb#000000″ text_font_size=\u00bb18px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb100%\u00bb custom_padding=\u00bb20px|0px|20px|0px|false|true\u00bb custom_padding_tablet=\u00bb20px||20px|40px|false|false\u00bb custom_padding_phone=\u00bb20px|0px|20px|0px|false|true\u00bb custom_padding_last_edited=\u00bbon|phone\u00bb hover_enabled=\u00bb0″ locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb sticky_enabled=\u00bb0″]<\/p>\n

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

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Antonio Di Cecco es un f\u00edsico te\u00f3rico con una s\u00f3lida formaci\u00f3n matem\u00e1tica que participa activamente en la formaci\u00f3n sobre AIML. El punto fuerte de su planteamiento es el estudio en profundidad de los fundamentos matem\u00e1ticos de los modelos AIML, capaces de proporcionar nuevas formas de presentar los conocimientos AIML, as\u00ed como del margen de mejora sobre el estado actual de la tecnolog\u00eda.<\/span><\/p>\n

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

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\u00bfQuieres m\u00e1s informaci\u00f3n al respecto?<\/h2>\n

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Explainable AI with PythonDe Leonida Gianfagna y Antonio Di Cecco\u00abExplainable AI with Python\u00bb ofrece una presentaci\u00f3n exhaustiva de los conceptos y t\u00e9cnicas disponibles en la actualidad para hacer m\u00e1s comprensibles los sistemas de aprendizaje autom\u00e1tico. Los enfoques presentados pueden aplicarse a casi todos los modelos actuales de aprendizaje autom\u00e1tico, como los modelos de regresi\u00f3n lineal […]<\/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-31869","page","type-page","status-publish","hentry"],"yoast_head":"\nExplainable AI with Python<\/title>\n<meta name=\"description\" content=\"\u00bfC\u00f3mo hacer m\u00e1s comprensibles los sistemas de aprendizaje autom\u00e1tico? \u00abExplainable AI with Python\u00bb de Gianfagna-Di Cecco ofrece una respuesta concreta\" \/>\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\/es\/explainable-ai-with-python\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explainable AI with Python\" \/>\n<meta property=\"og:description\" content=\"\u00bfC\u00f3mo hacer m\u00e1s comprensibles los sistemas de aprendizaje autom\u00e1tico? 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