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fix(curriculum): convert all video challenges to markdown (#39189)

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Shaun Hamilton
2020-08-04 20:56:41 +01:00
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12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/conclusion.english.md
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@ -7,10 +7,12 @@ videoId: LMNub5frQi4
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Most people that are experts in AI or machine learning usually...:
answers:
- have one specialization.
- have many specializations.
- have a deep understanding of many different frameworks.
- |
have one specialization.
- |
have many specializations.
- |
have a deep understanding of many different frameworks.
solution: 1
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.english.md
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@ -7,21 +7,26 @@ videoId: eCATNvwraXg
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What is **not** a good way to increase the accuracy of a convolutional neural network?
text: |
What is **not** a good way to increase the accuracy of a convolutional neural network?
answers:
- Augmenting the data you already have.
- Using a pre-trained model.
- Using your test data to retrain the model.
- |
Augmenting the data you already have.
- |
Using a pre-trained model.
- |
Using your test data to retrain the model.
solution: 3
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.english.md
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@ -7,21 +7,26 @@ videoId: LrdmcQpTyLw
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What are the three main properties of each convolutional layer?
text: |
What are the three main properties of each convolutional layer?
answers:
- Input size, the number of filters, and the sample size of the filters.
- Input size, input dimensions, and the color values of the input.
- Input size, input padding, and stride.
- |
Input size, the number of filters, and the sample size of the filters.
- |
Input size, input dimensions, and the color values of the input.
- |
Input size, input padding, and stride.
solution: 1
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.english.md
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@ -7,10 +7,12 @@ videoId: _1kTP7uoU9E
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...:
answers:
- also analyze input globally and extract features from specific areas.
- do not work well for image classification or object detection.
- scan through the entire input a little at a time and learn local patterns.
- |
also analyze input globally and extract features from specific areas.
- |
do not work well for image classification or object detection.
- |
scan through the entire input a little at a time and learn local patterns.
solution: 3
```
</section>

6
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.english.md
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@ -7,15 +7,18 @@ videoId: 5wHw8BTd2ZQ
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What kind of estimator/model does TensorFlow recommend using for classification?
text: |
What kind of estimator/model does TensorFlow recommend using for classification?
answers:
- |
`LinearClassifier`
@ -27,4 +30,3 @@ question:
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.english.md
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@ -7,21 +7,26 @@ videoId: qFF7ZQNvK9E
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What is classification?
text: |
What is classification?
answers:
- The process of separating data points into different classes.
- Predicting a numeric value or forecast based on independent and dependent variables.
- None of the above.
- |
The process of separating data points into different classes.
- |
Predicting a numeric value or forecast based on independent and dependent variables.
- |
None of the above.
solution: 1
```
</section>

21
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.english.md
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@ -7,23 +7,30 @@ videoId: 8sqIaHc9Cz4
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: Which of the following steps is **not** part of the K-Means algorithm?
text: |
Which of the following steps is **not** part of the K-Means algorithm?
answers:
- Randomly pick K points to place K centeroids.
- Assign each K point to the closest K centeroid.
- Move each K centeroid into the middle of all of their data points.
- Shuffle the K points so they're redistributed randomly.
- Reassign each K point to the closest K centeroid.
- |
Randomly pick K points to place K centeroids.
- |
Assign each K point to the closest K centeroid.
- |
Move each K centeroid into the middle of all of their data points.
- |
Shuffle the K points so they're redistributed randomly.
- |
Reassign each K point to the closest K centeroid.
solution: 4
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.english.md
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@ -7,21 +7,26 @@ videoId: IZg24y4wEPY
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What makes a Hidden Markov model different than linear regression or classification?
text: |
What makes a Hidden Markov model different than linear regression or classification?
answers:
- It uses probability distributions to predict future events or states.
- It analyzes the relationship between independent and dependent variables to make predictions.
- It separates data points into separate categories.
- |
It uses probability distributions to predict future events or states.
- |
It analyzes the relationship between independent and dependent variables to make predictions.
- |
It separates data points into separate categories.
solution: 1
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.english.md
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@ -7,21 +7,26 @@ videoId: _cEwvqVoBhI
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What are epochs?
text: |
What are epochs?
answers:
- The number of times the model will see the same data.
- A type of graph.
- The number of elements you feed to the model at once.
- |
The number of times the model will see the same data.
- |
A type of graph.
- |
The number of elements you feed to the model at once.
solution: 1
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.english.md
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@ -7,21 +7,26 @@ videoId: wz9J1slsi7I
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What is categorical data?
text: |
What is categorical data?
answers:
- Another term for one-hot encoding.
- Any data that is not numeric.
- Any data that is represented numerically.
- |
Another term for one-hot encoding.
- |
Any data that is not numeric.
- |
Any data that is represented numerically.
solution: 2
```
</section>

6
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.english.md
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@ -7,15 +7,18 @@ videoId: fYAYvLUawnc
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
text: |
What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
answers:
- |
`tensorflow.keras`
@ -27,4 +30,3 @@ question:
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.english.md
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@ -7,10 +7,12 @@ videoId: u85IOSsJsPI
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
What does the pandas `.head()` function do?
answers:
- Returns the number of entries in a data frame.
- Returns the number of columns in a data frame.
- By default, shows the first five rows or entries in a data frame.
- |
Returns the number of entries in a data frame.
- |
Returns the number of columns in a data frame.
- |
By default, shows the first five rows or entries in a data frame.
solution: 3
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/core-learning-algorithms.english.md
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@ -7,10 +7,12 @@ videoId: u5lZURgcWnU
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -20,12 +22,15 @@ question:
You have the average temperature in the month of March for the last 100 years. Using this data, you want to predict the average temperature in the month of March 5 years from now.
answers:
- Multiple regression
- Correlation
- Decision tree
- Linear regression
- |
Multiple regression
- |
Correlation
- |
Decision tree
- |
Linear regression
solution: 4
```
</section>

14
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.english.md
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@ -1,16 +1,18 @@
---
id: 5e8f2f13c4cdbe86b5c72d87
title: "Introduction: Machine Learning Fundamentals"
title: 'Introduction: Machine Learning Fundamentals'
challengeType: 11
isHidden: false
videoId: KwL1qTR5MT8
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Which statement below is **false**?
answers:
- Neural networks are modeled after the way the human brain works.
- Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
- Machine learning is a subset of artificial intelligence.
- |
Neural networks are modeled after the way the human brain works.
- |
Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
- |
Machine learning is a subset of artificial intelligence.
solution: 1
```
</section>

21
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.english.md
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@ -7,23 +7,30 @@ videoId: r9hRyGGjOgQ
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: Which of the following is **not** a type of tensor?
text: |
Which of the following is **not** a type of tensor?
answers:
- Variable
- Flowing
- Placeholder
- SparseTensor
- Constant
- |
Variable
- |
Flowing
- |
Placeholder
- |
SparseTensor
- |
Constant
solution: 2
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.english.md
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@ -7,10 +7,12 @@ videoId: WO1hINnBj20
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Before you make a prediction with your own review, you should...:
answers:
- decode the training dataset and compare the results to the test data.
- use the encodings from the training dataset to encode your review.
- assign random values between 0 and the maximum number of vocabulary in your dataset to each word in your review.
- |
decode the training dataset and compare the results to the test data.
- |
use the encodings from the training dataset to encode your review.
- |
assign random values between 0 and the maximum number of vocabulary in your dataset to each word in your review.
solution: 2
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.english.md
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@ -7,10 +7,12 @@ videoId: mUU9YXOFbZg
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Word embeddings are...:
answers:
- an unordered group of encoded words that describes the frequency of words in a given document.
- a group of encoded words that preserves the original order of the words in a given document.
- a vectorized representation of words in a given document that places words with similar meanings near each other.
- |
an unordered group of encoded words that describes the frequency of words in a given document.
- |
a group of encoded words that preserves the original order of the words in a given document.
- |
a vectorized representation of words in a given document that places words with similar meanings near each other.
solution: 3
```
</section>

6
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.english.md
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@ -7,15 +7,18 @@ videoId: bX5681NPOcA
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What is true about Recurrent Neural Networks?
text: |
What is true about Recurrent Neural Networks?
answers:
- |
1: They are a type of feed-forward neural network.
@ -29,4 +32,3 @@ question:
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.english.md
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@ -7,10 +7,12 @@ videoId: ZyCaF5S-lKg
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
Natural Language Processing is a branch of artificial intelligence that...:
answers:
- deals with how computers understand and process natural/human languages.
- translates image data into natural/human languages.
- is focused on translating computer languages into natural/human languages.
- |
deals with how computers understand and process natural/human languages.
- |
translates image data into natural/human languages.
- |
is focused on translating computer languages into natural/human languages.
solution: 1
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.english.md
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@ -7,21 +7,26 @@ videoId: S45tqW6BqRs
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: Which activation function switches values between -1 and 1?
text: |
Which activation function switches values between -1 and 1?
answers:
- ReLU (Rectified Linear Unit)
- Tanh (Hyperbolic Tangent)
- Sigmoid
- |
ReLU (Rectified Linear Unit)
- |
Tanh (Hyperbolic Tangent)
- |
Sigmoid
solution: 2
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.english.md
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@ -7,21 +7,26 @@ videoId: hdOtRPQe1o4
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What is an optimizer function?
text: |
What is an optimizer function?
answers:
- A function that increases the accuracy of a model's predictions.
- A function that implements the gradient descent and backpropagation algorithms for you.
- A function that reduces the time a model needs to train.
- |
A function that increases the accuracy of a model's predictions.
- |
A function that implements the gradient descent and backpropagation algorithms for you.
- |
A function that reduces the time a model needs to train.
solution: 2
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.english.md
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@ -7,10 +7,12 @@ videoId: uisdfrNrZW4
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
@ -18,11 +20,13 @@ question:
text: |
A densely connected neural network is one in which...:
answers:
- all the neurons in the current layer are connected to one neuron in the previous layer.
- all the neurons in each layer are connected randomly.
- all the neurons in the current layer are connected to every neuron in the previous layer.
- |
all the neurons in the current layer are connected to one neuron in the previous layer.
- |
all the neurons in each layer are connected randomly.
- |
all the neurons in the current layer are connected to every neuron in the previous layer.
solution: 3
```
</section>

12
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.english.md
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@ -7,20 +7,24 @@ videoId: DX7hJuaUZ7o
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: What can happen if the agent does not have a good balance of taking random actions and using learned actions?
text: |
What can happen if the agent does not have a good balance of taking random actions and using learned actions?
answers:
- The agent will always try to minimize its reward for the current state/action, leading to local minima.
- The agent will always try to maximize its reward for the current state/action, leading to local maxima.
- |
The agent will always try to minimize its reward for the current state/action, leading to local minima.
- |
The agent will always try to maximize its reward for the current state/action, leading to local maxima.
solution: 2
```
</section>

15
curriculum/challenges/english/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.english.md
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@ -7,21 +7,26 @@ videoId: Cf7DSU0gVb4
---
## Description
<section id='description'>
</section>
## Tests
<section id='tests'>
```yml
question:
text: The key components of reinforcement learning are...
text: |
The key components of reinforcement learning are...
answers:
- environment, representative, state, reaction, and reward.
- environment, agent, state, action, and reward.
- habitat, agent, state, action, and punishment.
- |
environment, representative, state, reaction, and reward.
- |
environment, agent, state, action, and reward.
- |
habitat, agent, state, action, and punishment.
solution: 2
```
</section>