feat(curriculum): restore seed + solution to Chinese (#40683)

* feat(tools): add seed/solution restore script

* chore(curriculum): remove empty sections' markers

* chore(curriculum): add seed + solution to Chinese

* chore: remove old formatter

* fix: update getChallenges

parse translated challenges separately, without reference to the source

* chore(curriculum): add dashedName to English

* chore(curriculum): add dashedName to Chinese

* refactor: remove unused challenge property 'name'

* fix: relax dashedName requirement

* fix: stray tag

Remove stray `pre` tag from challenge file.

Signed-off-by: nhcarrigan <nhcarrigan@gmail.com>

Co-authored-by: nhcarrigan <nhcarrigan@gmail.com>
This commit is contained in:
Oliver Eyton-Williams
2021-01-13 03:31:00 +01:00
committed by GitHub
parent 0095583028
commit ee1e8abd87
4163 changed files with 57505 additions and 10540 deletions

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@ -2,6 +2,7 @@
id: 5e9a0e9ef99a403d019610cc
challengeType: 11
videoId: bejQ-W9BGJg
dashedName: deep-learning-demystified
---
# --question--
@ -30,8 +31,3 @@ None of the above.
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e9a0e9ef99a403d019610cd
challengeType: 11
videoId: Y5M7KH4A4n4
dashedName: how-convolutional-neural-networks-work
---
# --question--
@ -26,8 +27,3 @@ If your data is text or sound based.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e9a0e9ef99a403d019610ca
challengeType: 11
videoId: zvalnHWGtx4
dashedName: how-deep-neural-networks-work
---
# --question--
@ -26,8 +27,3 @@ There is no difference between the two methods.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e9a0e9ef99a403d019610cb
challengeType: 11
videoId: UVimlsy9eW0
dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
---
# --question--
@ -26,8 +27,3 @@ Prediction, ignoring, forgetting, and selection.
3
# --hints--
# --solutions--

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@ -1,6 +1,7 @@
---
id: 5e46f8e3ac417301a38fb92f
challengeType: 10
dashedName: book-recommendation-engine-using-knn
---
# --description--
@ -25,3 +26,8 @@ It should pass all Python tests.
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```

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@ -1,6 +1,7 @@
---
id: 5e46f8dcac417301a38fb92e
challengeType: 10
dashedName: cat-and-dog-image-classifier
---
# --description--
@ -23,3 +24,8 @@ It should pass all Python tests.
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```

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@ -1,6 +1,7 @@
---
id: 5e46f8edac417301a38fb930
challengeType: 10
dashedName: linear-regression-health-costs-calculator
---
# --description--
@ -25,3 +26,8 @@ It should pass all Python tests.
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```

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@ -1,6 +1,7 @@
---
id: 5e46f8edac417301a38fb931
challengeType: 10
dashedName: neural-network-sms-text-classifier
---
# --description--
@ -23,3 +24,8 @@ It should pass all Python tests.
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```

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@ -1,6 +1,7 @@
---
id: 5e46f8d6ac417301a38fb92d
challengeType: 10
dashedName: rock-paper-scissors
---
# --description--
@ -23,3 +24,8 @@ It should pass all Python tests.
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da6
challengeType: 11
videoId: LMNub5frQi4
dashedName: conclusion
---
# --question--
@ -26,8 +27,3 @@ have a deep understanding of many different frameworks.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d99
challengeType: 11
videoId: eCATNvwraXg
dashedName: convolutional-neural-networks-evaluating-the-model
---
# --question--
@ -26,8 +27,3 @@ Using your test data to retrain the model.
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9a
challengeType: 11
videoId: h1XUt1AgIOI
dashedName: convolutional-neural-networks-picking-a-pretrained-model
---
# --question--
@ -45,8 +46,3 @@ C: `False`
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d97
challengeType: 11
videoId: LrdmcQpTyLw
dashedName: convolutional-neural-networks-the-convolutional-layer
---
# --question--
@ -26,8 +27,3 @@ Input size, input padding, and stride.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d96
challengeType: 11
videoId: _1kTP7uoU9E
dashedName: convolutional-neural-networks
---
# --question--
@ -26,8 +27,3 @@ scan through the entire input a little at a time and learn local patterns.
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8e
challengeType: 11
videoId: 5wHw8BTd2ZQ
dashedName: core-learning-algorithms-building-the-model
---
# --question--
@ -26,8 +27,3 @@ What kind of estimator/model does TensorFlow recommend using for classification?
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8d
challengeType: 11
videoId: qFF7ZQNvK9E
dashedName: core-learning-algorithms-classification
---
# --question--
@ -26,8 +27,3 @@ None of the above.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8f
challengeType: 11
videoId: 8sqIaHc9Cz4
dashedName: core-learning-algorithms-clustering
---
# --question--
@ -34,8 +35,3 @@ Reassign each K point to the closest K centeroid.
4
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d90
challengeType: 11
videoId: IZg24y4wEPY
dashedName: core-learning-algorithms-hidden-markov-models
---
# --question--
@ -26,8 +27,3 @@ It separates data points into separate categories.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8c
challengeType: 11
videoId: _cEwvqVoBhI
dashedName: core-learning-algorithms-the-training-process
---
# --question--
@ -26,8 +27,3 @@ The number of elements you feed to the model at once.
1
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8b
challengeType: 11
videoId: wz9J1slsi7I
dashedName: core-learning-algorithms-training-and-testing-data
---
# --question--
@ -26,8 +27,3 @@ Any data that is represented numerically.
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d91
challengeType: 11
videoId: fYAYvLUawnc
dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
---
# --question--
@ -26,8 +27,3 @@ What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8a
challengeType: 11
videoId: u85IOSsJsPI
dashedName: core-learning-algorithms-working-with-data
---
# --question--
@ -26,8 +27,3 @@ By default, shows the first five rows or entries in a data frame.
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d89
challengeType: 11
videoId: u5lZURgcWnU
dashedName: core-learning-algorithms
---
# --question--
@ -32,8 +33,3 @@ Linear regression
4
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d98
challengeType: 11
videoId: kfv0K8MtkIc
dashedName: creating-a-convolutional-neural-network
---
# --question--
@ -48,8 +49,3 @@ C: `MaxPooling2D`
3
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d87
challengeType: 11
videoId: KwL1qTR5MT8
dashedName: introduction-machine-learning-fundamentals
---
# --question--
@ -26,8 +27,3 @@ Machine learning is a subset of artificial intelligence.
1
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d88
challengeType: 11
videoId: r9hRyGGjOgQ
dashedName: introduction-to-tensorflow
---
# --question--
@ -34,8 +35,3 @@ Constant
2
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72da1
challengeType: 11
videoId: 32WBFS7lfsw
dashedName: natural-language-processing-with-rnns-building-the-model
---
# --question--
@ -58,8 +59,3 @@ D: `return model`
3
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72da0
challengeType: 11
videoId: j5xsxjq_Xk8
dashedName: natural-language-processing-with-rnns-create-a-play-generator
---
# --question--
@ -36,8 +37,3 @@ B: `from_generator`
1
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d9f
challengeType: 11
videoId: WO1hINnBj20
dashedName: natural-language-processing-with-rnns-making-predictions
---
# --question--
@ -26,8 +27,3 @@ assign random values between 0 and the maximum number of vocabulary in your data
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9c
challengeType: 11
videoId: mUU9YXOFbZg
dashedName: natural-language-processing-with-rnns-part-2
---
# --question--
@ -26,8 +27,3 @@ a vectorized representation of words in a given document that places words with
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9d
challengeType: 11
videoId: bX5681NPOcA
dashedName: natural-language-processing-with-rnns-recurring-neural-networks
---
# --question--
@ -30,8 +31,3 @@ What is true about Recurrent Neural Networks?
4
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9e
challengeType: 11
videoId: lYeLtu8Nq7c
dashedName: natural-language-processing-with-rnns-sentiment-analysis
---
# --question--
@ -46,8 +47,3 @@ C: `LSTM`
3
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da2
challengeType: 11
videoId: hEUiK7j9UI8
dashedName: natural-language-processing-with-rnns-training-the-model
---
# --question--
@ -50,8 +51,3 @@ C: `checkpoint_callback`
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9b
challengeType: 11
videoId: ZyCaF5S-lKg
dashedName: natural-language-processing-with-rnns
---
# --question--
@ -26,8 +27,3 @@ is focused on translating computer languages into natural/human languages.
1
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d93
challengeType: 11
videoId: S45tqW6BqRs
dashedName: neural-networks-activation-functions
---
# --question--
@ -26,8 +27,3 @@ Sigmoid
2
# --hints--
# --solutions--

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@ -2,6 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d95
challengeType: 11
videoId: K8bz1bmOCTw
dashedName: neural-networks-creating-a-model
---
# --question--
@ -46,8 +47,3 @@ C: `dense`
1
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d94
challengeType: 11
videoId: hdOtRPQe1o4
dashedName: neural-networks-optimizers
---
# --question--
@ -26,8 +27,3 @@ A function that reduces the time a model needs to train.
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# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72d92
challengeType: 11
videoId: uisdfrNrZW4
dashedName: neural-networks-with-tensorflow
---
# --question--
@ -26,8 +27,3 @@ all the neurons in the current layer are connected to every neuron in the previo
3
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72da5
challengeType: 11
videoId: RBBSNta234s
dashedName: reinforcement-learning-with-q-learning-example
---
# --question--
@ -42,8 +43,3 @@ C: `next_state`
1
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72da4
challengeType: 11
videoId: DX7hJuaUZ7o
dashedName: reinforcement-learning-with-q-learning-part-2
---
# --question--
@ -22,8 +23,3 @@ The agent will always try to maximize its reward for the current state/action, l
2
# --hints--
# --solutions--

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id: 5e8f2f13c4cdbe86b5c72da3
challengeType: 11
videoId: Cf7DSU0gVb4
dashedName: reinforcement-learning-with-q-learning
---
# --question--
@ -26,8 +27,3 @@ habitat, agent, state, action, and punishment.
2
# --hints--
# --solutions--