fix: QA/Infosec update and python to chinese
This commit is contained in:
Oliver Eyton-Williams
committed by
Mrugesh Mohapatra
Mrugesh Mohapatra
parent
2c78402837
commit
1cfa09adc4
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---
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id: 5e8f2f13c4cdbe86b5c72da6
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title: Conclusion
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challengeType: 11
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isHidden: false
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videoId: LMNub5frQi4
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Most people that are experts in AI or machine learning usually...:
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answers:
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- |
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have one specialization.
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- |
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have many specializations.
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- |
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have a deep understanding of many different frameworks.
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d99
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title: 'Convolutional Neural Networks: Evaluating the Model'
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challengeType: 11
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isHidden: false
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videoId: eCATNvwraXg
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What is **not** a good way to increase the accuracy of a convolutional neural network?
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answers:
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- |
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Augmenting the data you already have.
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- |
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Using a pre-trained model.
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- |
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Using your test data to retrain the model.
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solution: 3
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d9a
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title: 'Convolutional Neural Networks: Picking a Pretrained Model'
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challengeType: 11
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isHidden: false
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videoId: h1XUt1AgIOI
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
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```py
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base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),
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include_top=__C__,
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weights='imagenet'
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)
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```
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answers:
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- |
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A: `keras`
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B: `MobileNetV2`
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C: `False`
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- |
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A: `Keras`
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B: `MobileNetV2`
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C: `True`
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- |
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A: `keras`
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B: `mobile_net_v2`
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C: `False`
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d97
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title: 'Convolutional Neural Networks: The Convolutional Layer'
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challengeType: 11
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isHidden: false
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videoId: LrdmcQpTyLw
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What are the three main properties of each convolutional layer?
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answers:
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- |
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Input size, the number of filters, and the sample size of the filters.
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- |
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Input size, input dimensions, and the color values of the input.
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- |
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Input size, input padding, and stride.
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d96
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title: Convolutional Neural Networks
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challengeType: 11
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isHidden: false
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videoId: _1kTP7uoU9E
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...:
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answers:
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- |
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also analyze input globally and extract features from specific areas.
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- |
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do not work well for image classification or object detection.
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- |
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scan through the entire input a little at a time and learn local patterns.
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solution: 3
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8e
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title: 'Core Learning Algorithms: Building the Model'
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challengeType: 11
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isHidden: false
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videoId: 5wHw8BTd2ZQ
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What kind of estimator/model does TensorFlow recommend using for classification?
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answers:
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- |
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`LinearClassifier`
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- |
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`DNNClassifier`
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- |
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`BoostedTreesClassifier`
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solution: 2
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8d
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title: 'Core Learning Algorithms: Classification'
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challengeType: 11
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isHidden: false
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videoId: qFF7ZQNvK9E
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What is classification?
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answers:
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- |
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The process of separating data points into different classes.
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- |
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Predicting a numeric value or forecast based on independent and dependent variables.
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- |
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None of the above.
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8f
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title: 'Core Learning Algorithms: Clustering'
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challengeType: 11
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isHidden: false
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videoId: 8sqIaHc9Cz4
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Which of the following steps is **not** part of the K-Means algorithm?
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answers:
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- |
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Randomly pick K points to place K centeroids.
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- |
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Assign each K point to the closest K centeroid.
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- |
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Move each K centeroid into the middle of all of their data points.
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- |
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Shuffle the K points so they're redistributed randomly.
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- |
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Reassign each K point to the closest K centeroid.
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solution: 4
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d90
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title: 'Core Learning Algorithms: Hidden Markov Models'
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challengeType: 11
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isHidden: false
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videoId: IZg24y4wEPY
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What makes a Hidden Markov model different than linear regression or classification?
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answers:
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- |
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It uses probability distributions to predict future events or states.
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- |
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It analyzes the relationship between independent and dependent variables to make predictions.
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- |
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It separates data points into separate categories.
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8c
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title: 'Core Learning Algorithms: The Training Process'
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challengeType: 11
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isHidden: false
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videoId: _cEwvqVoBhI
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What are epochs?
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answers:
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- |
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The number of times the model will see the same data.
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- |
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A type of graph.
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- |
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The number of elements you feed to the model at once.
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solution: 1
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8b
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title: 'Core Learning Algorithms: Training and Testing Data'
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challengeType: 11
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isHidden: false
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videoId: wz9J1slsi7I
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What is categorical data?
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answers:
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- |
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Another term for one-hot encoding.
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- |
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Any data that is not numeric.
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- |
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Any data that is represented numerically.
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solution: 2
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d91
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title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
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challengeType: 11
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isHidden: false
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videoId: fYAYvLUawnc
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
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answers:
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- |
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`tensorflow.keras`
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- |
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`tensorflow_gpu`
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- |
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`tensorflow_probability`
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solution: 3
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d8a
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title: 'Core Learning Algorithms: Working with Data'
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challengeType: 11
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isHidden: false
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videoId: u85IOSsJsPI
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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What does the pandas `.head()` function do?
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answers:
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- |
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Returns the number of entries in a data frame.
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- |
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Returns the number of columns in a data frame.
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- |
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By default, shows the first five rows or entries in a data frame.
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solution: 3
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```
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</section>
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---
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id: 5e8f2f13c4cdbe86b5c72d89
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title: Core Learning Algorithms
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challengeType: 11
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isHidden: false
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videoId: u5lZURgcWnU
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Which type of analysis would be best suited for the following problem?:
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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.
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answers:
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- |
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Multiple regression
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- |
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Correlation
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- |
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Decision tree
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- |
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Linear regression
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solution: 4
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```
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</section>
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@ -0,0 +1,54 @@
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---
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id: 5e8f2f13c4cdbe86b5c72d98
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title: Creating a Convolutional Neural Network
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challengeType: 11
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isHidden: false
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videoId: kfv0K8MtkIc
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---
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## Description
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<section id='description'>
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</section>
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## Tests
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<section id='tests'>
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```yml
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question:
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text: |
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Fill in the blanks below to complete the architecture for a convolutional neural network:
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```py
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model = models.__A__()
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model.add(layers.__B__(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
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model.add(layers.__C__(2, 2))
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model.add(layers.__B__(64, (3, 3), activation='relu'))
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model.add(layers.__C__(2, 2))
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model.add(layers.__B__(32, (3, 3), activation='relu'))
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model.add(layers.__C__(2, 2))
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```
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answers:
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- |
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A: `Sequential`
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B: `add`
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C: `Wrapper`
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- |
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A: `keras`
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B: `Cropping2D`
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C: `AlphaDropout`
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- |
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A: `Sequential`
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B: `Conv2D`
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C: `MaxPooling2D`
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solution: 3
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```
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</section>
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@ -0,0 +1,32 @@
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---
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id: 5e8f2f13c4cdbe86b5c72d87
|
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title: 'Introduction: Machine Learning Fundamentals'
|
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challengeType: 11
|
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isHidden: false
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videoId: KwL1qTR5MT8
|
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---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
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```yml
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question:
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text: |
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Which statement below is **false**?
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answers:
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- |
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Neural networks are modeled after the way the human brain works.
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- |
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Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
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- |
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Machine learning is a subset of artificial intelligence.
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solution: 1
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```
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</section>
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@ -0,0 +1,36 @@
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---
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id: 5e8f2f13c4cdbe86b5c72d88
|
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title: Introduction to TensorFlow
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challengeType: 11
|
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isHidden: false
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videoId: r9hRyGGjOgQ
|
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---
|
||||
|
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## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
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||||
question:
|
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text: |
|
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Which of the following is **not** a type of tensor?
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answers:
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- |
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Variable
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- |
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Flowing
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- |
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Placeholder
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||||
- |
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SparseTensor
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||||
- |
|
||||
Constant
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||||
solution: 2
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```
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||||
</section>
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@ -0,0 +1,64 @@
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---
|
||||
id: 5e8f2f13c4cdbe86b5c72da1
|
||||
title: 'Natural Language Processing With RNNs: Building the Model'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: 32WBFS7lfsw
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks below to complete the `build_model` function:
|
||||
|
||||
```py
|
||||
def build_mode(vocab_size, embedding_dim, rnn_units, batch_size):
|
||||
model = tf.keras.Sequential([
|
||||
tf.keras.layers.Embedding(vocab_size,
|
||||
embedding_dim,
|
||||
batch_input_shape=[batch_size, None]),
|
||||
tf.keras.layers.__A__(rnn_units,
|
||||
return_sequences=__B__,
|
||||
recurrent_initializer='glorot_uniform),
|
||||
tf.keras.layers.Dense(__C__)
|
||||
])
|
||||
__D__
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `ELU`
|
||||
|
||||
B: `True`
|
||||
|
||||
C: `vocab_size`
|
||||
|
||||
D: `return model`
|
||||
- |
|
||||
A: `LSTM`
|
||||
|
||||
B: `False`
|
||||
|
||||
C: `batch_size`
|
||||
|
||||
D: `return model`
|
||||
- |
|
||||
A: `LSTM`
|
||||
|
||||
B: `True`
|
||||
|
||||
C: `vocab_size`
|
||||
|
||||
D: `return model`
|
||||
solution: 3
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,42 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da0
|
||||
title: 'Natural Language Processing With RNNs: Create a Play Generator'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: j5xsxjq_Xk8
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks below to create the training examples for the RNN:
|
||||
|
||||
```py
|
||||
char_dataset = tf.data.__A__.__B__(text_as_int)
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `DataSet`
|
||||
|
||||
B: `from_tensor_slices`
|
||||
- |
|
||||
A: `data`
|
||||
|
||||
B: `from_tensors`
|
||||
- |
|
||||
A: `DataSet`
|
||||
|
||||
B: `from_generator`
|
||||
solution: 1
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9f
|
||||
title: 'Natural Language Processing With RNNs: Making Predictions'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: WO1hINnBj20
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
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.
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9c
|
||||
title: 'Natural Language Processing With RNNs: Part 2'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: mUU9YXOFbZg
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
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.
|
||||
solution: 3
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,34 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9d
|
||||
title: 'Natural Language Processing With RNNs: Recurring Neural Networks'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: bX5681NPOcA
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
What is true about Recurrent Neural Networks?
|
||||
answers:
|
||||
- |
|
||||
1: They are a type of feed-forward neural network.
|
||||
- |
|
||||
2: They maintain an internal memory/state of the input that was already processed.
|
||||
- |
|
||||
3: RNN's contain a loop and process one piece of input at a time.
|
||||
- |
|
||||
4: Both 2 and 3.
|
||||
solution: 4
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,52 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9e
|
||||
title: 'Natural Language Processing With RNNs: Sentiment Analysis'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: lYeLtu8Nq7c
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks below to create the model for the RNN:
|
||||
|
||||
```py
|
||||
model = __A__.keras.Sequential([
|
||||
__A__.keras.layers.__B__(88584, 32),
|
||||
__A__.keras.layers.__C__(32),
|
||||
__A__.keras.layers.DENSE(1, activation='sigmoid')
|
||||
])
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `tensor_flow`
|
||||
|
||||
B: `embedding`
|
||||
|
||||
C: `LSTM`
|
||||
- |
|
||||
A: `tf`
|
||||
|
||||
B: `Embedding`
|
||||
|
||||
C: `AlphaDropout`
|
||||
- |
|
||||
A: `tf`
|
||||
|
||||
B: `Embedding`
|
||||
|
||||
C: `LSTM`
|
||||
solution: 3
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,56 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da2
|
||||
title: 'Natural Language Processing With RNNs: Training the Model'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: hEUiK7j9UI8
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks below to save your model's checkpoints in the `./checkpoints` directory and call the latest checkpoint for training:
|
||||
|
||||
```py
|
||||
checkpoint_dir = __A__
|
||||
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt_{epoch}')
|
||||
|
||||
checkpoint_callback = tf.keras.callbacks.__B__(
|
||||
filepath=checkpoint_prefix,
|
||||
save_weights_only=True
|
||||
)
|
||||
|
||||
history = model.fit(data, epochs=2, callbacks=[__C__])
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `'./training_checkpoints'`
|
||||
|
||||
B: `ModelCheckpoint`
|
||||
|
||||
C: `checkpoint_prefix`
|
||||
- |
|
||||
A: `'./checkpoints'`
|
||||
|
||||
B: `ModelCheckpoint`
|
||||
|
||||
C: `checkpoint_callback`
|
||||
- |
|
||||
A: `'./checkpoints'`
|
||||
|
||||
B: `BaseLogger`
|
||||
|
||||
C: `checkpoint_callback`
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9b
|
||||
title: Natural Language Processing With RNNs
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: ZyCaF5S-lKg
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
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.
|
||||
solution: 1
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d93
|
||||
title: 'Neural Networks: Activation Functions'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: S45tqW6BqRs
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Which activation function switches values between -1 and 1?
|
||||
answers:
|
||||
- |
|
||||
ReLU (Rectified Linear Unit)
|
||||
- |
|
||||
Tanh (Hyperbolic Tangent)
|
||||
- |
|
||||
Sigmoid
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,52 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d95
|
||||
title: 'Neural Networks: Creating a Model'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: K8bz1bmOCTw
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks below to build a sequential model of dense layers:
|
||||
|
||||
```py
|
||||
model = __A__.__B__([
|
||||
__A__.layers.Flatten(input_shape=(28, 28)),
|
||||
__A__.layers.__C__(128, activation='relu'),
|
||||
__A__.layers.__C__(10, activation='softmax')
|
||||
])
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `keras`
|
||||
|
||||
B: `Sequential`
|
||||
|
||||
C: `Dense`
|
||||
- |
|
||||
A: `tf`
|
||||
|
||||
B: `Sequential`
|
||||
|
||||
C: `Categorical`
|
||||
- |
|
||||
A: `keras`
|
||||
|
||||
B: `sequential`
|
||||
|
||||
C: `dense`
|
||||
solution: 1
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d94
|
||||
title: 'Neural Networks: Optimizers'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: hdOtRPQe1o4
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
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.
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d92
|
||||
title: Neural Networks with TensorFlow
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: uisdfrNrZW4
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
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.
|
||||
solution: 3
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,48 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da5
|
||||
title: 'Reinforcement Learning With Q-Learning: Example'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: RBBSNta234s
|
||||
---
|
||||
|
||||
## Description
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
text: |
|
||||
Fill in the blanks to complete the following Q-Learning equation:
|
||||
|
||||
```py
|
||||
Q[__A__, __B__] = Q[__A__, __B__] + LEARNING_RATE * (reward + GAMMA * np.max(Q[__C__, :]) - Q[__A__, __B__])
|
||||
```
|
||||
|
||||
answers:
|
||||
- |
|
||||
A: `state`
|
||||
|
||||
B: `action`
|
||||
|
||||
C: `next_state`
|
||||
- |
|
||||
A: `state`
|
||||
|
||||
B: `action`
|
||||
|
||||
C: `prev_state`
|
||||
- |
|
||||
A: `state`
|
||||
|
||||
B: `reaction`
|
||||
|
||||
C: `next_state`
|
||||
solution: 1
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
@ -0,0 +1,30 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da4
|
||||
title: 'Reinforcement Learning With Q-Learning: Part 2'
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
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?
|
||||
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.
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
@ -0,0 +1,32 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da3
|
||||
title: Reinforcement Learning With Q-Learning
|
||||
challengeType: 11
|
||||
isHidden: false
|
||||
videoId: Cf7DSU0gVb4
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<section id='description'>
|
||||
</section>
|
||||
|
||||
## Tests
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
question:
|
||||
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.
|
||||
solution: 2
|
||||
```
|
||||
|
||||
</section>
|
||||
Reference in New Issue
Block a user