fix: QA/Infosec update and python to chinese
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Mrugesh Mohapatra
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---
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id: 5e9a0e9ef99a403d019610cc
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title: Deep Learning Demystified
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challengeType: 11
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isHidden: false
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videoId: bejQ-W9BGJg
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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How should you assign weights to input neurons before training your network for the first time?
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answers:
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- |
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From smallest to largest.
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Completely randomly.
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Alphabetically.
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None of the above.
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solution: 2
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```
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</section>
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---
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id: 5e9a0e9ef99a403d019610cd
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title: How Convolutional Neural Networks work
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challengeType: 11
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isHidden: false
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videoId: Y5M7KH4A4n4
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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When are Convolutional Neural Networks not useful?
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answers:
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- |
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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.
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If your data is made up of different 2D or 3D images.
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If your data is text or sound based.
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solution: 1
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```
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</section>
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---
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id: 5e9a0e9ef99a403d019610ca
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title: How Deep Neural Networks Work
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challengeType: 11
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isHidden: false
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videoId: zvalnHWGtx4
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Why is it better to calculate the gradient (slope) directly rather than numerically?
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answers:
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It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
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It is more accurate.
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There is no difference between the two methods.
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solution: 1
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```
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</section>
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---
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id: 5e9a0e9ef99a403d019610cb
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title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
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challengeType: 11
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isHidden: false
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videoId: UVimlsy9eW0
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What are the main neural network components that make up a Long Short Term Memory network?
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answers:
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New information and prediction.
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Prediction, collected possibilities, and selection.
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Prediction, ignoring, forgetting, and selection.
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solution: 3
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```
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</section>
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