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
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commit
ee1e8abd87
@ -2,6 +2,7 @@
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id: 5e8f2f13c4cdbe86b5c72da6
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challengeType: 11
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videoId: LMNub5frQi4
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dashedName: conclusion
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---
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# --question--
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@ -26,8 +27,3 @@ have a deep understanding of many different frameworks.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d99
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challengeType: 11
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videoId: eCATNvwraXg
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dashedName: convolutional-neural-networks-evaluating-the-model
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---
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# --question--
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@ -26,8 +27,3 @@ Using your test data to retrain the model.
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9a
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challengeType: 11
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videoId: h1XUt1AgIOI
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dashedName: convolutional-neural-networks-picking-a-pretrained-model
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---
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# --question--
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@ -45,8 +46,3 @@ C: `False`
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d97
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challengeType: 11
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videoId: LrdmcQpTyLw
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dashedName: convolutional-neural-networks-the-convolutional-layer
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---
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# --question--
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@ -26,8 +27,3 @@ Input size, input padding, and stride.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d96
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challengeType: 11
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videoId: _1kTP7uoU9E
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dashedName: convolutional-neural-networks
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---
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# --question--
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@ -26,8 +27,3 @@ scan through the entire input a little at a time and learn local patterns.
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8e
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challengeType: 11
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videoId: 5wHw8BTd2ZQ
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dashedName: core-learning-algorithms-building-the-model
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---
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# --question--
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@ -26,8 +27,3 @@ What kind of estimator/model does TensorFlow recommend using for classification?
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2
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8d
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challengeType: 11
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videoId: qFF7ZQNvK9E
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dashedName: core-learning-algorithms-classification
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---
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# --question--
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@ -26,8 +27,3 @@ None of the above.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8f
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challengeType: 11
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videoId: 8sqIaHc9Cz4
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dashedName: core-learning-algorithms-clustering
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---
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# --question--
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@ -34,8 +35,3 @@ Reassign each K point to the closest K centeroid.
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4
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d90
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challengeType: 11
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videoId: IZg24y4wEPY
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dashedName: core-learning-algorithms-hidden-markov-models
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---
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# --question--
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@ -26,8 +27,3 @@ It separates data points into separate categories.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8c
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challengeType: 11
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videoId: _cEwvqVoBhI
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dashedName: core-learning-algorithms-the-training-process
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---
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# --question--
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@ -26,8 +27,3 @@ The number of elements you feed to the model at once.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8b
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challengeType: 11
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videoId: wz9J1slsi7I
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dashedName: core-learning-algorithms-training-and-testing-data
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---
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# --question--
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@ -26,8 +27,3 @@ Any data that is represented numerically.
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2
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d91
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challengeType: 11
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videoId: fYAYvLUawnc
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dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
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---
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# --question--
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@ -26,8 +27,3 @@ What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d8a
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challengeType: 11
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videoId: u85IOSsJsPI
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dashedName: core-learning-algorithms-working-with-data
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---
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# --question--
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@ -26,8 +27,3 @@ By default, shows the first five rows or entries in a data frame.
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d89
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challengeType: 11
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videoId: u5lZURgcWnU
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dashedName: core-learning-algorithms
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---
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# --question--
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@ -32,8 +33,3 @@ Linear regression
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d98
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challengeType: 11
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videoId: kfv0K8MtkIc
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dashedName: creating-a-convolutional-neural-network
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---
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# --question--
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@ -48,8 +49,3 @@ C: `MaxPooling2D`
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d87
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challengeType: 11
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videoId: KwL1qTR5MT8
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dashedName: introduction-machine-learning-fundamentals
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---
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# --question--
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@ -26,8 +27,3 @@ Machine learning is a subset of artificial intelligence.
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1
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d88
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challengeType: 11
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videoId: r9hRyGGjOgQ
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dashedName: introduction-to-tensorflow
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---
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# --question--
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@ -34,8 +35,3 @@ Constant
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da1
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challengeType: 11
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videoId: 32WBFS7lfsw
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dashedName: natural-language-processing-with-rnns-building-the-model
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---
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# --question--
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@ -58,8 +59,3 @@ D: `return model`
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da0
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challengeType: 11
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videoId: j5xsxjq_Xk8
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dashedName: natural-language-processing-with-rnns-create-a-play-generator
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---
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# --question--
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@ -36,8 +37,3 @@ B: `from_generator`
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9f
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challengeType: 11
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videoId: WO1hINnBj20
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dashedName: natural-language-processing-with-rnns-making-predictions
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9c
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challengeType: 11
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videoId: mUU9YXOFbZg
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dashedName: natural-language-processing-with-rnns-part-2
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9d
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challengeType: 11
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videoId: bX5681NPOcA
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dashedName: natural-language-processing-with-rnns-recurring-neural-networks
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9e
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challengeType: 11
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videoId: lYeLtu8Nq7c
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dashedName: natural-language-processing-with-rnns-sentiment-analysis
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da2
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challengeType: 11
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videoId: hEUiK7j9UI8
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dashedName: natural-language-processing-with-rnns-training-the-model
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d9b
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challengeType: 11
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videoId: ZyCaF5S-lKg
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dashedName: natural-language-processing-with-rnns
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d93
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challengeType: 11
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videoId: S45tqW6BqRs
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dashedName: neural-networks-activation-functions
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d95
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challengeType: 11
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videoId: K8bz1bmOCTw
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dashedName: neural-networks-creating-a-model
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---
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72d94
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challengeType: 11
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videoId: hdOtRPQe1o4
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dashedName: neural-networks-optimizers
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# --question--
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id: 5e8f2f13c4cdbe86b5c72d92
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challengeType: 11
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videoId: uisdfrNrZW4
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dashedName: neural-networks-with-tensorflow
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# --question--
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id: 5e8f2f13c4cdbe86b5c72da5
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challengeType: 11
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videoId: RBBSNta234s
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dashedName: reinforcement-learning-with-q-learning-example
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da4
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challengeType: 11
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videoId: DX7hJuaUZ7o
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dashedName: reinforcement-learning-with-q-learning-part-2
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# --question--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da3
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challengeType: 11
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videoId: Cf7DSU0gVb4
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dashedName: reinforcement-learning-with-q-learning
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# --question--
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# --solutions--
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