{"id":1637,"date":"2023-11-02T11:14:34","date_gmt":"2023-11-02T10:14:34","guid":{"rendered":"https:\/\/ia4legos.umh.es\/?page_id=1637"},"modified":"2023-12-01T18:35:12","modified_gmt":"2023-12-01T17:35:12","slug":"api-keras-para-redes-recurrentes","status":"publish","type":"page","link":"https:\/\/unidigital.umh.es\/ca\/api-keras-para-redes-recurrentes\/","title":{"rendered":"(Espa\u00f1ol) API Keras para redes recurrentes<\/span>"},"content":{"rendered":"

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IR A CONTENIDOS DEEP LEARNING<\/strong><\/span><\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.17.4″ _module_preset=”default” background_color=”#FFFFFF” global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_button button_url=”https:\/\/unidigital.umh.es\/api-keras-para-redes-convolucionales-ii\/” button_text=”M\u00f3dulo 3.2 API Keras para redes convolucionales (II)” button_alignment=”center” _builder_version=”4.17.4″ _module_preset=”default” custom_button=”on” button_text_size=”14px” button_text_color=”#bf4c00″ button_bg_color=”#FFFFFF” button_border_color=”#bf5800″ button_font=”Roboto||||||||” button_icon=”H||divi||400″ button_icon_placement=”left” transform_scale_tablet=”” transform_scale_phone=”” transform_scale_last_edited=”on|desktop” transform_translate=”-92px|0px” transform_translate_tablet=”-21px|0px” transform_translate_phone=”-22px|0px” transform_translate_last_edited=”on|phone” transform_translate_linked=”off” transform_rotate_tablet=”” transform_rotate_phone=”” transform_rotate_last_edited=”on|desktop” transform_skew_tablet=”” transform_skew_phone=”” transform_skew_last_edited=”on|desktop” transform_origin_tablet=”” transform_origin_phone=”” transform_origin_last_edited=”on|desktop” transform_styles_last_edited=”on|phone” custom_margin=”|-50px|||false|false” transform_styles_tablet=”” transform_styles_phone=”” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.4″ _module_preset=”default” text_font=”Signika||||||||” header_2_font_size=”22px” text_orientation=”right” global_colors_info=”{}”]<\/p>\n

M\u00f3dulo 4. Redes Recurrentes<\/strong><\/h1>\n

<\/strong><\/p>\n

Unidad 4.1 API Keras para redes recurrentes<\/strong><\/h2>\n

 <\/p>\n

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    \n
  1. Redes neuronales recurrentes<\/a><\/span><\/li>\n
  2. Conceptos b\u00e1sicos de la RNN<\/a><\/span><\/li>\n
  3. Otras capas recurrentes<\/a><\/span><\/li>\n<\/ol>\n

    [\/et_pb_accordion_item][\/et_pb_accordion][et_pb_image src=”https:\/\/ia4legos.umh.es\/files\/2023\/07\/Enlace-a-video-cuaderno-12.png” title_text=”Enlace a v\u00eddeo cuaderno 12″ url=”https:\/\/www.youtube.com\/watch?v=Tymi_huQ5sE&list=PLClKgnzRFYe6aNxojV8JV1d74EUtosmus&index=12″ url_new_window=”on” align=”center” _builder_version=”4.17.4″ _module_preset=”default” transform_translate=”0px|27px” transform_translate_linked=”off” width=”35%” custom_padding=”|||1px||” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/ia4legos.umh.es\/files\/2023\/07\/Enlace-a-cuaderno-13.png” title_text=”Enlace a cuaderno 13″ url=”https:\/\/colab.research.google.com\/drive\/1CSx4maOmtAv-NSKRy1PYww9YA_pLW–W” url_new_window=”on” align=”center” _builder_version=”4.17.4″ _module_preset=”default” width=”35%” module_alignment=”center” custom_padding=”24px||||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_accordion _builder_version=”4.17.4″ _module_preset=”default” toggle_font=”Signika||||||||” global_colors_info=”{}”][et_pb_accordion_item title=”Tema 13. Series temporales” open=”on” _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

      \n
    1. Diferentes tipos de tareas de series temporales<\/a><\/span><\/li>\n
    2. Un ejemplo de previsi\u00f3n de la temperatura<\/a><\/span><\/li>\n<\/ol>\n

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

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

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

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      IR A CONTENIDOS DEEP LEARNINGM\u00f3dulo 4. Redes Recurrentes Unidad 4.1 API Keras para redes recurrentes   Redes neuronales recurrentes Conceptos b\u00e1sicos de la RNN Otras capas recurrentes Diferentes tipos de tareas de series temporales Un ejemplo de previsi\u00f3n de la temperatura<\/p>\n","protected":false},"author":24713,"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":"","_links_to":"","_links_to_target":""},"_links":{"self":[{"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/pages\/1637"}],"collection":[{"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/users\/24713"}],"replies":[{"embeddable":true,"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/comments?post=1637"}],"version-history":[{"count":0,"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/pages\/1637\/revisions"}],"wp:attachment":[{"href":"https:\/\/unidigital.umh.es\/ca\/wp-json\/wp\/v2\/media?parent=1637"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}