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>
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commit
ee1e8abd87
@ -2,6 +2,7 @@
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id: 5e9a0e9ef99a403d019610cc
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challengeType: 11
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videoId: bejQ-W9BGJg
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dashedName: deep-learning-demystified
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---
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# --question--
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@ -30,8 +31,3 @@ None of the above.
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2
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# --hints--
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# --solutions--
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@ -2,6 +2,7 @@
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id: 5e9a0e9ef99a403d019610cd
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challengeType: 11
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videoId: Y5M7KH4A4n4
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dashedName: how-convolutional-neural-networks-work
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---
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# --question--
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@ -26,8 +27,3 @@ If your data is text or sound based.
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1
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# --hints--
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# --solutions--
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@ -2,6 +2,7 @@
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id: 5e9a0e9ef99a403d019610ca
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challengeType: 11
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videoId: zvalnHWGtx4
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dashedName: how-deep-neural-networks-work
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---
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# --question--
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@ -26,8 +27,3 @@ There is no difference between the two methods.
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1
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# --hints--
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# --solutions--
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id: 5e9a0e9ef99a403d019610cb
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challengeType: 11
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videoId: UVimlsy9eW0
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dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
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---
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# --question--
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@ -26,8 +27,3 @@ Prediction, ignoring, forgetting, and selection.
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3
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# --hints--
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# --solutions--
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@ -1,6 +1,7 @@
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---
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id: 5e46f8e3ac417301a38fb92f
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challengeType: 10
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dashedName: book-recommendation-engine-using-knn
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---
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# --description--
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@ -25,3 +26,8 @@ It should pass all Python tests.
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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@ -1,6 +1,7 @@
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---
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id: 5e46f8dcac417301a38fb92e
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challengeType: 10
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dashedName: cat-and-dog-image-classifier
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---
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# --description--
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@ -23,3 +24,8 @@ It should pass all Python tests.
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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---
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id: 5e46f8edac417301a38fb930
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challengeType: 10
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dashedName: linear-regression-health-costs-calculator
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---
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# --description--
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@ -25,3 +26,8 @@ It should pass all Python tests.
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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@ -1,6 +1,7 @@
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---
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id: 5e46f8edac417301a38fb931
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challengeType: 10
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dashedName: neural-network-sms-text-classifier
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---
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# --description--
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@ -23,3 +24,8 @@ It should pass all Python tests.
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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@ -1,6 +1,7 @@
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---
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id: 5e46f8d6ac417301a38fb92d
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challengeType: 10
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dashedName: rock-paper-scissors
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---
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# --description--
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@ -23,3 +24,8 @@ It should pass all Python tests.
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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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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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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# --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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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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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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# --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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3
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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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1
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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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2
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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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3
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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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1
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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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2
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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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3
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# --hints--
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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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@ -30,8 +31,3 @@ What is true about Recurrent Neural Networks?
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4
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# --hints--
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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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@ -46,8 +47,3 @@ C: `LSTM`
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3
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# --hints--
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# --solutions--
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id: 5e8f2f13c4cdbe86b5c72da2
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challengeType: 11
|
||||
videoId: hEUiK7j9UI8
|
||||
dashedName: natural-language-processing-with-rnns-training-the-model
|
||||
---
|
||||
|
||||
# --question--
|
||||
@ -50,8 +51,3 @@ C: `checkpoint_callback`
|
||||
|
||||
2
|
||||
|
||||
# --hints--
|
||||
|
||||
|
||||
# --solutions--
|
||||
|
||||
|
||||
@ -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--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
id: 5e8f2f13c4cdbe86b5c72d93
|
||||
challengeType: 11
|
||||
videoId: S45tqW6BqRs
|
||||
dashedName: neural-networks-activation-functions
|
||||
---
|
||||
|
||||
# --question--
|
||||
@ -26,8 +27,3 @@ Sigmoid
|
||||
|
||||
2
|
||||
|
||||
# --hints--
|
||||
|
||||
|
||||
# --solutions--
|
||||
|
||||
|
||||
@ -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--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
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.
|
||||
|
||||
2
|
||||
|
||||
# --hints--
|
||||
|
||||
|
||||
# --solutions--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
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--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
id: 5e8f2f13c4cdbe86b5c72da5
|
||||
challengeType: 11
|
||||
videoId: RBBSNta234s
|
||||
dashedName: reinforcement-learning-with-q-learning-example
|
||||
---
|
||||
|
||||
# --question--
|
||||
@ -42,8 +43,3 @@ C: `next_state`
|
||||
|
||||
1
|
||||
|
||||
# --hints--
|
||||
|
||||
|
||||
# --solutions--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
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--
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
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--
|
||||
|
||||
|
||||
Reference in New Issue
Block a user