chore: enable python curriculum (#39118)

This commit is contained in:
Mrugesh Mohapatra
2020-06-23 17:36:39 +05:30
committed by GitHub
parent 7323cc5619
commit decf1ece99
161 changed files with 235 additions and 235 deletions

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da6
title: Conclusion
challengeType: 11
isHidden: true
isHidden: false
videoId: LMNub5frQi4
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d99
title: 'Convolutional Neural Networks: Evaluating the Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: eCATNvwraXg
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9a
title: 'Convolutional Neural Networks: Picking a Pretrained Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: h1XUt1AgIOI
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d97
title: 'Convolutional Neural Networks: The Convolutional Layer'
challengeType: 11
isHidden: true
isHidden: false
videoId: LrdmcQpTyLw
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d96
title: Convolutional Neural Networks
challengeType: 11
isHidden: true
isHidden: false
videoId: _1kTP7uoU9E
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8e
title: 'Core Learning Algorithms: Building the Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: 5wHw8BTd2ZQ
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8d
title: 'Core Learning Algorithms: Classification'
challengeType: 11
isHidden: true
isHidden: false
videoId: qFF7ZQNvK9E
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8f
title: 'Core Learning Algorithms: Clustering'
challengeType: 11
isHidden: true
isHidden: false
videoId: 8sqIaHc9Cz4
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d90
title: 'Core Learning Algorithms: Hidden Markov Models'
challengeType: 11
isHidden: true
isHidden: false
videoId: IZg24y4wEPY
---
@ -19,7 +19,7 @@ question:
answers:
- It uses probability distributions to predict future events or states.
- It analyzes the relationship between independent and dependent variables to make predictions.
- It separates data points into separate categories.
- It separates data points into separate categories.
solution: 1
```

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8c
title: 'Core Learning Algorithms: The Training Process'
challengeType: 11
isHidden: true
isHidden: false
videoId: _cEwvqVoBhI
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8b
title: 'Core Learning Algorithms: Training and Testing Data'
challengeType: 11
isHidden: true
isHidden: false
videoId: wz9J1slsi7I
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d91
title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
challengeType: 11
isHidden: true
isHidden: false
videoId: fYAYvLUawnc
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d8a
title: 'Core Learning Algorithms: Working with Data'
challengeType: 11
isHidden: true
isHidden: false
videoId: u85IOSsJsPI
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d89
title: Core Learning Algorithms
challengeType: 11
isHidden: true
isHidden: false
videoId: u5lZURgcWnU
---
@ -16,8 +16,8 @@ videoId: u5lZURgcWnU
```yml
question:
text: |
Which type of analysis would be best suited for the following problem?:
Which type of analysis would be best suited for the following problem?:
You have the average temperature in the month of March for the last 100 years. Using this data, you want to predict the average temperature in the month of March 5 years from now.
answers:
- Multiple regression

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d98
title: Creating a Convolutional Neural Network
challengeType: 11
isHidden: true
isHidden: false
videoId: kfv0K8MtkIc
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d87
title: "Introduction: Machine Learning Fundamentals"
challengeType: 11
isHidden: true
isHidden: false
videoId: KwL1qTR5MT8
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d88
title: Introduction to TensorFlow
challengeType: 11
isHidden: true
isHidden: false
videoId: r9hRyGGjOgQ
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da1
title: 'Natural Language Processing With RNNs: Building the Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: 32WBFS7lfsw
---
@ -22,7 +22,7 @@ question:
def build_mode(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size,
embedding_dim,
embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.__A__(rnn_units,
return_sequences=__B__,

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da0
title: 'Natural Language Processing With RNNs: Create a Play Generator'
challengeType: 11
isHidden: true
isHidden: false
videoId: j5xsxjq_Xk8
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9f
title: 'Natural Language Processing With RNNs: Making Predictions'
challengeType: 11
isHidden: true
isHidden: false
videoId: WO1hINnBj20
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9c
title: 'Natural Language Processing With RNNs: Part 2'
challengeType: 11
isHidden: true
isHidden: false
videoId: mUU9YXOFbZg
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9d
title: 'Natural Language Processing With RNNs: Recurring Neural Networks'
challengeType: 11
isHidden: true
isHidden: false
videoId: bX5681NPOcA
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9e
title: 'Natural Language Processing With RNNs: Sentiment Analysis'
challengeType: 11
isHidden: true
isHidden: false
videoId: lYeLtu8Nq7c
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da2
title: 'Natural Language Processing With RNNs: Training the Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: hEUiK7j9UI8
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d9b
title: Natural Language Processing With RNNs
challengeType: 11
isHidden: true
isHidden: false
videoId: ZyCaF5S-lKg
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d93
title: 'Neural Networks: Activation Functions'
challengeType: 11
isHidden: true
isHidden: false
videoId: S45tqW6BqRs
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d95
title: 'Neural Networks: Creating a Model'
challengeType: 11
isHidden: true
isHidden: false
videoId: K8bz1bmOCTw
---
@ -29,21 +29,21 @@ question:
answers:
- |
A: `keras`
B: `Sequential`
C: `Dense`
- |
A: `tf`
B: `Sequential`
C: `Categorical`
- |
A: `keras`
B: `sequential`
C: `dense`
solution: 1
```

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d94
title: 'Neural Networks: Optimizers'
challengeType: 11
isHidden: true
isHidden: false
videoId: hdOtRPQe1o4
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72d92
title: Neural Networks with TensorFlow
challengeType: 11
isHidden: true
isHidden: false
videoId: uisdfrNrZW4
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da5
title: 'Reinforcement Learning With Q-Learning: Example'
challengeType: 11
isHidden: true
isHidden: false
videoId: RBBSNta234s
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da4
title: 'Reinforcement Learning With Q-Learning: Part 2'
challengeType: 11
isHidden: true
isHidden: false
videoId: DX7hJuaUZ7o
---

View File

@ -2,7 +2,7 @@
id: 5e8f2f13c4cdbe86b5c72da3
title: Reinforcement Learning With Q-Learning
challengeType: 11
isHidden: true
isHidden: false
videoId: Cf7DSU0gVb4
---