chore(i18n,curriculum): processed translations (#42868)

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

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cc
-title: Deep Learning Demystified
+title: 解密深度學習
 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?
+在你第一次訓練你的網絡之前，你應該如何給輸入層節點分配權重？
 
 ## --answers--
 
-From smallest to largest.
+從小到大
 
 ---
 
-Completely randomly.
+完全隨機的
 
 ---
 
-Alphabetically.
+按字母順序
 
 ---
 
-None of the above.
+以上都不對
 
 ## --video-solution--
 

curriculum/challenges/chinese-traditional/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: 卷積神經網絡的工作原理
 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?
+卷積神經網絡在什麼時候是沒有用的？
 
 ## --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.
+當數據的組成形式不能像圖片存儲的數據格式一樣，或者說你的數據可以重新排列，它仍然可以被運用到卷積神經網絡中。
 
 ---
 
-If your data is made up of different 2D or 3D images.
+如果你的數據是由 2D 或者 3D 圖片組成的。
 
 ---
 
-If your data is text or sound based.
+如果你的數據是基於文本或者音頻的形式。
 
 ## --video-solution--
 

curriculum/challenges/chinese-traditional/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: 深度神經網絡的工作原理
 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?
+相比較數字的計算，爲什麼深度神經網絡可以更好地計算梯度（斜率）？
 
 ## --answers--
 
-It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
+通過回溯整個神經網絡來更改每一層神經網絡的權重，在計算上來說是非常耗時的。
 
 ---
 
-It is more accurate.
+它更加準確。
 
 ---
 
-There is no difference between the two methods.
+這兩種方法之間沒有區別。
 
 ## --video-solution--
 

curriculum/challenges/chinese-traditional/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: 循環神經網絡 RNN 和長短期記憶 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?
+構成長短期記憶網絡的主要神經網絡組件是什麼？
 
 ## --answers--
 
-New information and prediction.
+新的信息和預測
 
 ---
 
-Prediction, collected possibilities, and selection.
+預測、收集的可能性和選擇
 
 ---
 
-Prediction, ignoring, forgetting, and selection.
+預測、忽視、遺忘和選擇
 
 ## --video-solution--