chore(i18n,learn): processed translations (#45432)

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cc
-title: Deep Learning Demystified
+title: Deep Learning Desmitificado
 challengeType: 11
 videoId: bejQ-W9BGJg
 dashedName: deep-learning-demystified
@@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
 
 ## --text--
 
-How should you assign weights to input neurons before training your network for the first time?
+¿Cómo deberías asignar pesos a las neuronas de entrada antes de entrenar tu red por primera vez?
 
 ## --answers--
 
-From smallest to largest.
+De más pequeño a más grande.
 
 ---
 
-Completely randomly.
+Completamente al azar.
 
 ---
 
-Alphabetically.
+Alfabéticamente.
 
 ---
 
-None of the above.
+Ninguna de las anteriores.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cd
-title: How Convolutional Neural Networks work
+title: Cómo funcionan las Redes Neuronales Convolucionales
 challengeType: 11
 videoId: Y5M7KH4A4n4
 dashedName: how-convolutional-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
 
 ## --text--
 
-When are Convolutional Neural Networks not useful?
+¿Cuándo las Redes Neurales Convolucionales no son útiles?
 
 ## --answers--
 
-If your data can't be made to look like an image, or if you can rearrange elements of your data and it's still just as useful.
+Si tus datos no pueden ser hechos para parecer una imagen, o si puedes reorganizar elementos de tus datos y es igualmente útil.
 
 ---
 
-If your data is made up of different 2D or 3D images.
+Si sus datos se componen de diferentes imágenes 2D o 3D.
 
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-If your data is text or sound based.
+Si sus datos son basados en texto o sonido.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610ca
-title: How Deep Neural Networks Work
+title: Cómo Funcionan las Redes Neuronales Profundas
 challengeType: 11
 videoId: zvalnHWGtx4
 dashedName: how-deep-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-deep-neural-networks-work
 
 ## --text--
 
-Why is it better to calculate the gradient (slope) directly rather than numerically?
+¿Por qué es mejor calcular la gradiente (pendiente) directamente que numéricamente?
 
 ## --answers--
 
-It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
+Es computacionalmente caro volver a través de toda la red neuronal y ajustar los pesos para cada capa de la red neuronal.
 
 ---
 
-It is more accurate.
+Es más preciso.
 
 ---
 
-There is no difference between the two methods.
+No hay diferencia entre ambos métodos.
 
 ## --video-solution--
 

curriculum/challenges/espanol/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cb
-title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
+title: Redes Neurales Recurrentes RNN y Memoria a Largo Plazo LSTM
 challengeType: 11
 videoId: UVimlsy9eW0
 dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
@@ -10,19 +10,19 @@ dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
 
 ## --text--
 
-What are the main neural network components that make up a Long Short Term Memory network?
+¿Cuáles son los principales componentes de la red neuronal que componen una red de memoria a largo plazo?
 
 ## --answers--
 
-New information and prediction.
+Nueva información y predicción.
 
 ---
 
-Prediction, collected possibilities, and selection.
+Predicción, posibilidades recolectadas y selección.
 
 ---
 
-Prediction, ignoring, forgetting, and selection.
+Predicción, ignoración, olvido, y selección.
 
 ## --video-solution--