From 00c9bc590950db25c21fed27290cdbf1cac26472 Mon Sep 17 00:00:00 2001 From: camperbot Date: Thu, 17 Mar 2022 01:39:26 +0530 Subject: [PATCH] chore(i18n,learn): processed translations (#45445) --- .../tensorflow/conclusion.md | 14 +++++++++----- .../tensorflow/convolutional-neural-networks.md | 14 +++++++++----- .../creating-a-convolutional-neural-network.md | 8 ++++++-- 3 files changed, 24 insertions(+), 12 deletions(-) diff --git a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/conclusion.md b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/conclusion.md index b87ca8022b..e396b44637 100644 --- a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/conclusion.md +++ b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/conclusion.md @@ -1,8 +1,12 @@ --- id: 5e8f2f13c4cdbe86b5c72da6 -title: Conclusion +title: Conclusión challengeType: 11 videoId: LMNub5frQi4 +bilibiliIds: + aid: 208033449 + bvid: BV1Wh411p79h + cid: 409139716 dashedName: conclusion --- @@ -10,19 +14,19 @@ dashedName: conclusion ## --text-- -Most people that are experts in AI or machine learning usually...: +La mayoría de la gente que es experta en IA o aprendizaje automático suele...: ## --answers-- -have one specialization. +tienen una especialización. --- -have many specializations. +tienen muchas especializaciones. --- -have a deep understanding of many different frameworks. +tienen un profunda comprensión de muchos marcos de trabajo diferentes. ## --video-solution-- diff --git a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md index 616ef13476..47c96144a2 100644 --- a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md +++ b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md @@ -1,8 +1,12 @@ --- id: 5e8f2f13c4cdbe86b5c72d96 -title: Convolutional Neural Networks +title: Redes neuronales convolucionales challengeType: 11 videoId: _1kTP7uoU9E +bilibiliIds: + aid: 805604251 + bvid: BV1D34y1S7zS + cid: 409131358 dashedName: convolutional-neural-networks --- @@ -10,19 +14,19 @@ dashedName: convolutional-neural-networks ## --text-- -Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...: +Redes densas de interconexión, analizan entradas a escala global y reconoce patrones específicos en áreas específicas. Redes neuronales convolucionales...: ## --answers-- -also analyze input globally and extract features from specific areas. +incluso analiza las entradas globalmente y extrae características de áreas específicas. --- -do not work well for image classification or object detection. +no trabaja bien en el reconocimiento de imágenes o en la detección de objetos. --- -scan through the entire input a little at a time and learn local patterns. +escanea en pequeñas porciones de la entrada, un poco a la vez, y aprende patrones locales. ## --video-solution-- diff --git a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md index 68999197b2..ffe7b1e130 100644 --- a/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md +++ b/curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md @@ -1,8 +1,12 @@ --- id: 5e8f2f13c4cdbe86b5c72d98 -title: Creating a Convolutional Neural Network +title: Creando una Red Neural Convolucional challengeType: 11 videoId: kfv0K8MtkIc +bilibiliIds: + aid: 420605824 + bvid: BV1p341127wW + cid: 409131869 dashedName: creating-a-convolutional-neural-network --- @@ -10,7 +14,7 @@ dashedName: creating-a-convolutional-neural-network ## --text-- -Fill in the blanks below to complete the architecture for a convolutional neural network: +Rellena los siguientes espacios en blanco para completar la arquitectura para una red neuronal convolucional: ```py model = models.__A__()