feat: enable new langs (#42491)

Enable italian and portuguese
This commit is contained in:
Nicholas Carrigan (he/him)
2021-06-15 00:49:18 -07:00
committed by GitHub
parent d8d6d20793
commit f25e3e69f8
3301 changed files with 423168 additions and 6 deletions

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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

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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

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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

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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