chore(i8n,learn): processed translations

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

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+---
+id: 5e9a0e9ef99a403d019610cc
+title: Deep Learning Demystified
+challengeType: 11
+videoId: bejQ-W9BGJg
+dashedName: deep-learning-demystified
+---
+
+# --question--
+
+## --text--
+
+How should you assign weights to input neurons before training your network for the first time?
+
+## --answers--
+
+From smallest to largest.
+
+---
+
+Completely randomly.
+
+---
+
+Alphabetically.
+
+---
+
+None of the above.
+
+## --video-solution--
+
+2
+

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

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+---
+id: 5e9a0e9ef99a403d019610cd
+title: How Convolutional Neural Networks work
+challengeType: 11
+videoId: Y5M7KH4A4n4
+dashedName: how-convolutional-neural-networks-work
+---
+
+# --question--
+
+## --text--
+
+When are Convolutional Neural Networks not useful?
+
+## --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.
+
+---
+
+If your data is made up of different 2D or 3D images.
+
+---
+
+If your data is text or sound based.
+
+## --video-solution--
+
+1
+

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

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+---
+id: 5e9a0e9ef99a403d019610ca
+title: How Deep Neural Networks Work
+challengeType: 11
+videoId: zvalnHWGtx4
+dashedName: how-deep-neural-networks-work
+---
+
+# --question--
+
+## --text--
+
+Why is it better to calculate the gradient (slope) directly rather than numerically?
+
+## --answers--
+
+It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
+
+---
+
+It is more accurate.
+
+---
+
+There is no difference between the two methods.
+
+## --video-solution--
+
+1
+

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

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+---
+id: 5e9a0e9ef99a403d019610cb
+title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
+challengeType: 11
+videoId: UVimlsy9eW0
+dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
+---
+
+# --question--
+
+## --text--
+
+What are the main neural network components that make up a Long Short Term Memory network?
+
+## --answers--
+
+New information and prediction.
+
+---
+
+Prediction, collected possibilities, and selection.
+
+---
+
+Prediction, ignoring, forgetting, and selection.
+
+## --video-solution--
+
+3
+