{"id":31868,"date":"2023-06-21T17:40:14","date_gmt":"2023-06-21T17:40:14","guid":{"rendered":"https:\/\/www.cyberguru.it\/?page_id=31868"},"modified":"2024-03-16T16:55:30","modified_gmt":"2024-03-16T16:55:30","slug":"explainable-ai-with-python","status":"publish","type":"page","link":"https:\/\/www.cyberguru.it\/fr\/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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Par Leonida Gianfagna et Antonio Di Cecco<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\u00a0\u00bb1″ _builder_version=\u00a0\u00bb4.21.0″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb background_image=\u00a0\u00bbhttps:\/\/www.cyberguru.it\/wp-content\/uploads\/2022\/10\/Rete_Neuronale-02-2.webp\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_row column_structure=\u00a0\u00bb1_2,1_2″ use_custom_gutter=\u00a0\u00bbon\u00a0\u00bb make_equal=\u00a0\u00bbon\u00a0\u00bb custom_padding_last_edited=\u00a0\u00bboff|desktop\u00a0\u00bb _builder_version=\u00a0\u00bb4.19.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb100%\u00a0\u00bb max_width=\u00a0\u00bb2560px\u00a0\u00bb custom_margin_tablet=\u00a0\u00bb\u00a0\u00bb custom_margin_phone=\u00a0\u00bb||||false|true\u00a0\u00bb custom_margin_last_edited=\u00a0\u00bbon|phone\u00a0\u00bb custom_padding=\u00a0\u00bb|0.2%||0px|false|false\u00a0\u00bb custom_padding_tablet=\u00a0\u00bb|50px||50px|false|true\u00a0\u00bb custom_padding_phone=\u00a0\u00bb|30px||30px|false|true\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_column type=\u00a0\u00bb1_2″ _builder_version=\u00a0\u00bb4.19.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb custom_css_main_element=\u00a0\u00bbalign-self:center;\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_image src=\u00a0\u00bbhttps:\/\/www.cyberguru.it\/wp-content\/uploads\/2023\/06\/ebook4.png\u00a0\u00bb alt=\u00a0\u00bbEbook Leonida\u00a0\u00bb title_text=\u00a0\u00bbEbook Leonida\u00a0\u00bb _builder_version=\u00a0\u00bb4.21.0″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00a0\u00bb1_2″ _builder_version=\u00a0\u00bb4.19.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb custom_padding=\u00a0\u00bb|8%|||false|false\u00a0\u00bb custom_padding_tablet=\u00a0\u00bb|8%|||false|false\u00a0\u00bb custom_padding_phone=\u00a0\u00bb|5%||5%|false|true\u00a0\u00bb custom_padding_last_edited=\u00a0\u00bbon|phone\u00a0\u00bb custom_css_main_element=\u00a0\u00bbalign-self:center;\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_blurb alt=\u00a0\u00bbCyberguru favicon\u00a0\u00bb icon_placement=\u00a0\u00bbleft\u00a0\u00bb content_max_width=\u00a0\u00bb1100px\u00a0\u00bb _builder_version=\u00a0\u00bb4.21.0″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb body_font=\u00a0\u00bbRubik||||||||\u00a0\u00bb body_text_color=\u00a0\u00bbgcid-65b65f10-97af-4b49-9890-b01efe796309″ body_font_size=\u00a0\u00bb18px\u00a0\u00bb module_alignment=\u00a0\u00bbleft\u00a0\u00bb custom_css_main_element=\u00a0\u00bbfont-weight:400;\u00a0\u00bb global_colors_info=\u00a0\u00bb{%22gcid-65b65f10-97af-4b49-9890-b01efe796309%22:%91%22body_text_color%22%93}\u00a0\u00bb]<\/p>\n

Explainable AI with Python<\/strong> <\/span>fournit une pr\u00e9sentation compl\u00e8te des concepts et des techniques disponibles aujourd\u2019hui pour rendre les syst\u00e8mes d\u2019apprentissage automatique plus compr\u00e9hensibles. Les approches pr\u00e9sent\u00e9es peuvent \u00eatre appliqu\u00e9es \u00e0 presque tous les mod\u00e8les actuels de machine learning, tels que le mod\u00e8le de r\u00e9gression lin\u00e9aire et logistique, les r\u00e9seaux neuronaux de deep learning, le traitement du langage naturel et la reconnaissance d\u2019image.<\/span><\/p>\n

Les progr\u00e8s r\u00e9alis\u00e9s dans le cadre du machine learning contribuent \u00e0 accro\u00eetre l\u2019utilisation d\u2019agents artificiels capables d\u2019effectuer des t\u00e2ches critiques pr\u00e9c\u00e9demment g\u00e9r\u00e9es par l\u2019homme (comme la sant\u00e9 ou les activit\u00e9s juridiques et financi\u00e8res). Bien que les principes qui guident la conception de ces agents soient clairs, la plupart des mod\u00e8les de deep learning utilis\u00e9s restent \u00ab opaques \u00bb \u00e0 la compr\u00e9hension humaine. Le livre comble le vide actuel dans la litt\u00e9rature traitant de ce sujet, en adoptant \u00e0 la fois une perspective th\u00e9orique et pratique, et en permettant au lecteur de travailler rapidement avec les outils et le code utilis\u00e9s pour les syst\u00e8mes Explainable AI.<\/span><\/span><\/p>\n

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En commen\u00e7ant par des exemples de ce qu\u2019est l\u2019Explainable AI (XAI) et pourquoi il est si n\u00e9cessaire, le livre d\u00e9taille diff\u00e9rentes approches du XAI en fonction du contexte et des besoins sp\u00e9cifiques. Des exemples pratiques de mod\u00e8les interpr\u00e9tables utilisant Python sont ensuite pr\u00e9sent\u00e9s, montrant comment les mod\u00e8les intrins\u00e8quement interpr\u00e9tables peuvent \u00eatre interpr\u00e9t\u00e9s et comment produire des explications \u00ab compr\u00e9hensibles par l\u2019homme \u00bb. Il est d\u00e9montr\u00e9 que les m\u00e9thodes de mod\u00e8les agnostiques pour le XAI produisent des explications qui ne reposent pas sur les parties internes des mod\u00e8les ML qui sont \u00ab opaques \u00bb. En utilisant des exemples de vision par ordinateur, les auteurs examinent ensuite les mod\u00e8les exploitables pour le deep learning et les m\u00e9thodes potentielles pour l\u2019avenir. Avec une perspective pratique, les auteurs d\u00e9montrent \u00e9galement comment utiliser efficacement ML et XAI en science. Le chapitre final explique l\u2019Adversarial Machine Learning et comment faire XAI en utilisant des exemples conflictuels.<\/span><\/p>\n

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Si vous \u00eates int\u00e9ress\u00e9 par le livre Explainable AI with Python cliquez ici.<\/a><\/strong><\/em><\/p>\n

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<\/h2>\n

Les auteurs<\/h2>\n

 <\/p>\n

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CARD<\/p>\n

\u00a0\u00bb content_phone=\u00a0\u00bbLeonida Gianfagna\u00a0\u00bb content_last_edited=\u00a0\u00bbon|desktop\u00a0\u00bb _builder_version=\u00a0\u00bb4.21.0″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb text_font=\u00a0\u00bbInter|600|||||||\u00a0\u00bb text_text_color=\u00a0\u00bb#000000″ text_font_size=\u00a0\u00bb18px\u00a0\u00bb text_orientation=\u00a0\u00bbcenter\u00a0\u00bb custom_padding=\u00a0\u00bb20px|0px|20px|0px|false|true\u00a0\u00bb custom_padding_tablet=\u00a0\u00bb20px||20px|40px|false|false\u00a0\u00bb custom_padding_phone=\u00a0\u00bb20px|0px|20px|0px|false|true\u00a0\u00bb custom_padding_last_edited=\u00a0\u00bbon|phone\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ locked=\u00a0\u00bboff\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″]<\/p>\n

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

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Leonida Gianfagna (PhD, MBA) est une physicienne th\u00e9orique qui travaille actuellement dans la cybers\u00e9curit\u00e9 en tant que directrice de la R&D pour Cyber Guru. Avant de rejoindre Cyber Guru, elle a travaill\u00e9 pendant 15 ans chez IBM o\u00f9 elle a occup\u00e9 des postes de direction dans le domaine du d\u00e9veloppement de logiciels ITSM (IT Service Management). Elle est auteure de plusieurs publications en physique th\u00e9orique et informatique et accr\u00e9dit\u00e9e IBM Master Inventor.<\/span><\/p>\n

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CARD<\/p>\n

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

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Antonio Di Cecco est un physicien th\u00e9oricien avec une solide formation math\u00e9matique, qui s\u2019engage activement dans la formation sur l\u2019AIML. Le point fort de son approche est l\u2019approfondissement des fondements math\u00e9matiques des mod\u00e8les AIML, capables de fournir de nouvelles fa\u00e7ons de pr\u00e9senter les connaissances AIML et l\u2019espace d\u2019am\u00e9lioration par rapport \u00e0 l\u2019\u00e9tat de l\u2019art existant.<\/span><\/p>\n

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

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Vous souhaitez en savoir plus ?<\/h2>\n

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Explainable AI with PythonPar Leonida Gianfagna et Antonio Di CeccoExplainable AI with Python fournit une pr\u00e9sentation compl\u00e8te des concepts et des techniques disponibles aujourd\u2019hui pour rendre les syst\u00e8mes d\u2019apprentissage automatique plus compr\u00e9hensibles. Les approches pr\u00e9sent\u00e9es peuvent \u00eatre appliqu\u00e9es \u00e0 presque tous les mod\u00e8les actuels de machine learning, tels que le mod\u00e8le de r\u00e9gression lin\u00e9aire et […]<\/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-31868","page","type-page","status-publish","hentry"],"yoast_head":"\nExplainable AI with Python<\/title>\n<meta name=\"description\" content=\"Comment rendre les syst\u00e8mes de machine learning plus compr\u00e9hensibles\u00a0? 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