fix: QA/Infosec update and python to chinese

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

@@ -0,0 +1,35 @@
+---
+id: 5e9a0e9ef99a403d019610cc
+title: Deep Learning Demystified
+challengeType: 11
+isHidden: false
+videoId: bejQ-W9BGJg
+---
+
+## Description
+
+<section id='description'>
+</section>
+
+## Tests
+
+<section id='tests'>
+
+```yml
+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.
+  solution: 2
+```
+
+</section>

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

@@ -0,0 +1,33 @@
+---
+id: 5e9a0e9ef99a403d019610cd
+title: How Convolutional Neural Networks work
+challengeType: 11
+isHidden: false
+videoId: Y5M7KH4A4n4
+---
+
+## Description
+
+<section id='description'>
+</section>
+
+## Tests
+
+<section id='tests'>
+
+```yml
+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.
+  solution: 1
+```
+
+</section>

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

@@ -0,0 +1,33 @@
+---
+id: 5e9a0e9ef99a403d019610ca
+title: How Deep Neural Networks Work
+challengeType: 11
+isHidden: false
+videoId: zvalnHWGtx4
+---
+
+## Description
+
+<section id='description'>
+</section>
+
+## Tests
+
+<section id='tests'>
+
+```yml
+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.
+  solution: 1
+```
+
+</section>

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

@@ -0,0 +1,33 @@
+---
+id: 5e9a0e9ef99a403d019610cb
+title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
+challengeType: 11
+isHidden: false
+videoId: UVimlsy9eW0
+---
+
+## Description
+
+<section id='description'>
+</section>
+
+## Tests
+
+<section id='tests'>
+
+```yml
+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.
+  solution: 3
+```
+
+</section>