chore(i18n,chn): manually downloaded curriculum (#42858)

This commit is contained in:
Mrugesh Mohapatra
2021-07-15 13:04:11 +05:30
committed by GitHub
parent eef1805fe6
commit 7eb0630f2d
248 changed files with 5645 additions and 2149 deletions

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---
id: 5e9a0e9ef99a403d019610cc
title: Deep Learning Demystified
title: 解密深度学习
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?
在你第一次训练你的网络之前,你应该如何给输入层节点分配权重?
## --answers--
From smallest to largest.
从小到大
---
Completely randomly.
完全随机的
---
Alphabetically.
按字母顺序
---
None of the above.
以上都不对
## --video-solution--

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---
id: 5e9a0e9ef99a403d019610cd
title: How Convolutional Neural Networks work
title: 卷积神经网络的工作原理
challengeType: 11
videoId: Y5M7KH4A4n4
dashedName: how-convolutional-neural-networks-work
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## --text--
When are Convolutional Neural Networks not useful?
卷积神经网络在什么时候是没有用的?
## --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.
当数据的组成形式不能像图片存储的数据格式一样,或者说你的数据可以重新排列,它仍然可以被运用到卷积神经网络中。
---
If your data is made up of different 2D or 3D images.
如果你的数据是由 2D 或者 3D 图片组成的。
---
If your data is text or sound based.
如果你的数据是基于文本或者音频的形式。
## --video-solution--

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---
id: 5e9a0e9ef99a403d019610ca
title: How Deep Neural Networks Work
title: 深度神经网络的工作原理
challengeType: 11
videoId: zvalnHWGtx4
dashedName: how-deep-neural-networks-work
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## --text--
Why is it better to calculate the gradient (slope) directly rather than numerically?
相比较数字的计算,为什么深度神经网络可以更好地计算梯度(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.
通过回溯整个神经网络来更改每一层神经网络的权重,在计算上来说是非常耗时的。
---
It is more accurate.
它更加准确。
---
There is no difference between the two methods.
这两种方法之间没有区别。
## --video-solution--

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---
id: 5e9a0e9ef99a403d019610cb
title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
title: 循环神经网络 RNN 和长短期记忆 LSTM
challengeType: 11
videoId: UVimlsy9eW0
dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
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## --text--
What are the main neural network components that make up a Long Short Term Memory network?
构成长短期记忆网络的主要神经网络组件是什么?
## --answers--
New information and prediction.
新的信息和预测
---
Prediction, collected possibilities, and selection.
预测、收集的可能性和选择
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Prediction, ignoring, forgetting, and selection.
预测、忽视、遗忘和选择
## --video-solution--