committed by
GitHub
parent
d8d6d20793
commit
f25e3e69f8
@ -0,0 +1,34 @@
|
||||
---
|
||||
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
|
||||
|
@ -0,0 +1,30 @@
|
||||
---
|
||||
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
|
||||
|
@ -0,0 +1,30 @@
|
||||
---
|
||||
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
|
||||
|
@ -0,0 +1,30 @@
|
||||
---
|
||||
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
|
||||
|
Reference in New Issue
Block a user