chore(i8n,learn): processed translations

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/conclusion.md

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+---
+id: 5e8f2f13c4cdbe86b5c72da6
+title: Conclusion
+challengeType: 11
+videoId: LMNub5frQi4
+dashedName: conclusion
+---
+
+# --question--
+
+## --text--
+
+Most people that are experts in AI or machine learning usually...:
+
+## --answers--
+
+have one specialization.
+
+---
+
+have many specializations.
+
+---
+
+have a deep understanding of many different frameworks.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d99
+title: 'Convolutional Neural Networks: Evaluating the Model'
+challengeType: 11
+videoId: eCATNvwraXg
+dashedName: convolutional-neural-networks-evaluating-the-model
+---
+
+# --question--
+
+## --text--
+
+What is **not** a good way to increase the accuracy of a convolutional neural network?
+
+## --answers--
+
+Augmenting the data you already have.
+
+---
+
+Using a pre-trained model.
+
+---
+
+Using your test data to retrain the model.
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d9a
+title: 'Convolutional Neural Networks: Picking a Pretrained Model'
+challengeType: 11
+videoId: h1XUt1AgIOI
+dashedName: convolutional-neural-networks-picking-a-pretrained-model
+---
+
+# --question--
+
+## --text--
+
+Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
+
+```py
+base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),
+                                               include_top=__C__,
+                                               weights='imagenet'
+                                               )
+```
+
+## --answers--
+
+A: `keras`
+
+B: `MobileNetV2`
+
+C: `False`
+
+---
+
+A: `Keras`
+
+B: `MobileNetV2`
+
+C: `True`
+
+---
+
+A: `keras`
+
+B: `mobile_net_v2`
+
+C: `False`
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d97
+title: 'Convolutional Neural Networks: The Convolutional Layer'
+challengeType: 11
+videoId: LrdmcQpTyLw
+dashedName: convolutional-neural-networks-the-convolutional-layer
+---
+
+# --question--
+
+## --text--
+
+What are the three main properties of each convolutional layer?
+
+## --answers--
+
+Input size, the number of filters, and the sample size of the filters.
+
+---
+
+Input size, input dimensions, and the color values of the input.
+
+---
+
+Input size, input padding, and stride.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d96
+title: Convolutional Neural Networks
+challengeType: 11
+videoId: _1kTP7uoU9E
+dashedName: convolutional-neural-networks
+---
+
+# --question--
+
+## --text--
+
+Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...:
+
+## --answers--
+
+also analyze input globally and extract features from specific areas.
+
+---
+
+do not work well for image classification or object detection.
+
+---
+
+scan through the entire input a little at a time and learn local patterns.
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d8e
+title: 'Core Learning Algorithms: Building the Model'
+challengeType: 11
+videoId: 5wHw8BTd2ZQ
+dashedName: core-learning-algorithms-building-the-model
+---
+
+# --question--
+
+## --text--
+
+What kind of estimator/model does TensorFlow recommend using for classification?
+
+## --answers--
+
+`LinearClassifier`
+
+---
+
+`DNNClassifier`
+
+---
+
+`BoostedTreesClassifier`
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d8d
+title: 'Core Learning Algorithms: Classification'
+challengeType: 11
+videoId: qFF7ZQNvK9E
+dashedName: core-learning-algorithms-classification
+---
+
+# --question--
+
+## --text--
+
+What is classification?
+
+## --answers--
+
+The process of separating data points into different classes.
+
+---
+
+Predicting a numeric value or forecast based on independent and dependent variables.
+
+---
+
+None of the above.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.md

@@ -0,0 +1,38 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d8f
+title: 'Core Learning Algorithms: Clustering'
+challengeType: 11
+videoId: 8sqIaHc9Cz4
+dashedName: core-learning-algorithms-clustering
+---
+
+# --question--
+
+## --text--
+
+Which of the following steps is **not** part of the K-Means algorithm?
+
+## --answers--
+
+Randomly pick K points to place K centeroids.
+
+---
+
+Assign each K point to the closest K centeroid.
+
+---
+
+Move each K centeroid into the middle of all of their data points.
+
+---
+
+Shuffle the K points so they're redistributed randomly.
+
+---
+
+Reassign each K point to the closest K centeroid.
+
+## --video-solution--
+
+4
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d90
+title: 'Core Learning Algorithms: Hidden Markov Models'
+challengeType: 11
+videoId: IZg24y4wEPY
+dashedName: core-learning-algorithms-hidden-markov-models
+---
+
+# --question--
+
+## --text--
+
+What makes a Hidden Markov model different than linear regression or classification?
+
+## --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.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.md

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+---
+id: 5e8f2f13c4cdbe86b5c72d8c
+title: 'Core Learning Algorithms: The Training Process'
+challengeType: 11
+videoId: _cEwvqVoBhI
+dashedName: core-learning-algorithms-the-training-process
+---
+
+# --question--
+
+## --text--
+
+What are epochs?
+
+## --answers--
+
+The number of times the model will see the same data.
+
+---
+
+A type of graph.
+
+---
+
+The number of elements you feed to the model at once.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d8b
+title: 'Core Learning Algorithms: Training and Testing Data'
+challengeType: 11
+videoId: wz9J1slsi7I
+dashedName: core-learning-algorithms-training-and-testing-data
+---
+
+# --question--
+
+## --text--
+
+What is categorical data?
+
+## --answers--
+
+Another term for one-hot encoding.
+
+---
+
+Any data that is not numeric.
+
+---
+
+Any data that is represented numerically.
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d91
+title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
+challengeType: 11
+videoId: fYAYvLUawnc
+dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
+---
+
+# --question--
+
+## --text--
+
+What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
+
+## --answers--
+
+`tensorflow.keras`
+
+---
+
+`tensorflow_gpu`
+
+---
+
+`tensorflow_probability`
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d8a
+title: 'Core Learning Algorithms: Working with Data'
+challengeType: 11
+videoId: u85IOSsJsPI
+dashedName: core-learning-algorithms-working-with-data
+---
+
+# --question--
+
+## --text--
+
+What does the pandas `.head()` function do?
+
+## --answers--
+
+Returns the number of entries in a data frame.
+
+---
+
+Returns the number of columns in a data frame.
+
+---
+
+By default, shows the first five rows or entries in a data frame.
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/core-learning-algorithms.md

@@ -0,0 +1,36 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d89
+title: Core Learning Algorithms
+challengeType: 11
+videoId: u5lZURgcWnU
+dashedName: core-learning-algorithms
+---
+
+# --question--
+
+## --text--
+
+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
+
+---
+
+Correlation
+
+---
+
+Decision tree
+
+---
+
+Linear regression
+
+## --video-solution--
+
+4
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md

@@ -0,0 +1,52 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d98
+title: Creating a Convolutional Neural Network
+challengeType: 11
+videoId: kfv0K8MtkIc
+dashedName: creating-a-convolutional-neural-network
+---
+
+# --question--
+
+## --text--
+
+Fill in the blanks below to complete the architecture for a convolutional neural network:
+
+```py
+model = models.__A__()
+model.add(layers.__B__(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.__C__(2, 2))
+model.add(layers.__B__(64, (3, 3), activation='relu'))
+model.add(layers.__C__(2, 2))
+model.add(layers.__B__(32, (3, 3), activation='relu'))
+model.add(layers.__C__(2, 2))
+```
+
+## --answers--
+
+A: `Sequential`
+
+B: `add`
+
+C: `Wrapper`
+
+---
+
+A: `keras`
+
+B: `Cropping2D`
+
+C: `AlphaDropout`
+
+---
+
+A: `Sequential`
+
+B: `Conv2D`
+
+C: `MaxPooling2D`
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d87
+title: 'Introduction: Machine Learning Fundamentals'
+challengeType: 11
+videoId: KwL1qTR5MT8
+dashedName: introduction-machine-learning-fundamentals
+---
+
+# --question--
+
+## --text--
+
+Which statement below is **false**?
+
+## --answers--
+
+Neural networks are modeled after the way the human brain works.
+
+---
+
+Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
+
+---
+
+Machine learning is a subset of artificial intelligence.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.md

@@ -0,0 +1,38 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d88
+title: Introduction to TensorFlow
+challengeType: 11
+videoId: r9hRyGGjOgQ
+dashedName: introduction-to-tensorflow
+---
+
+# --question--
+
+## --text--
+
+Which of the following is **not** a type of tensor?
+
+## --answers--
+
+Variable
+
+---
+
+Flowing
+
+---
+
+Placeholder
+
+---
+
+SparseTensor
+
+---
+
+Constant
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-building-the-model.md

@@ -0,0 +1,62 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da1
+title: 'Natural Language Processing With RNNs: Building the Model'
+challengeType: 11
+videoId: 32WBFS7lfsw
+dashedName: natural-language-processing-with-rnns-building-the-model
+---
+
+# --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`
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-create-a-play-generator.md

@@ -0,0 +1,40 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da0
+title: 'Natural Language Processing With RNNs: Create a Play Generator'
+challengeType: 11
+videoId: j5xsxjq_Xk8
+dashedName: natural-language-processing-with-rnns-create-a-play-generator
+---
+
+# --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`
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d9f
+title: 'Natural Language Processing With RNNs: Making Predictions'
+challengeType: 11
+videoId: WO1hINnBj20
+dashedName: natural-language-processing-with-rnns-making-predictions
+---
+
+# --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.
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d9c
+title: 'Natural Language Processing With RNNs: Part 2'
+challengeType: 11
+videoId: mUU9YXOFbZg
+dashedName: natural-language-processing-with-rnns-part-2
+---
+
+# --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.
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.md

@@ -0,0 +1,34 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d9d
+title: 'Natural Language Processing With RNNs: Recurring Neural Networks'
+challengeType: 11
+videoId: bX5681NPOcA
+dashedName: natural-language-processing-with-rnns-recurring-neural-networks
+---
+
+# --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.
+
+## --video-solution--
+
+4
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-sentimental-analysis.md

@@ -0,0 +1,50 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d9e
+title: 'Natural Language Processing With RNNs: Sentiment Analysis'
+challengeType: 11
+videoId: lYeLtu8Nq7c
+dashedName: natural-language-processing-with-rnns-sentiment-analysis
+---
+
+# --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`
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-training-the-model.md

@@ -0,0 +1,54 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da2
+title: 'Natural Language Processing With RNNs: Training the Model'
+challengeType: 11
+videoId: hEUiK7j9UI8
+dashedName: natural-language-processing-with-rnns-training-the-model
+---
+
+# --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`
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d9b
+title: Natural Language Processing With RNNs
+challengeType: 11
+videoId: ZyCaF5S-lKg
+dashedName: natural-language-processing-with-rnns
+---
+
+# --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.
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d93
+title: 'Neural Networks: Activation Functions'
+challengeType: 11
+videoId: S45tqW6BqRs
+dashedName: neural-networks-activation-functions
+---
+
+# --question--
+
+## --text--
+
+Which activation function switches values between -1 and 1?
+
+## --answers--
+
+ReLU (Rectified Linear Unit)
+
+---
+
+Tanh (Hyperbolic Tangent)
+
+---
+
+Sigmoid
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/neural-networks-creating-a-model.md

@@ -0,0 +1,50 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d95
+title: 'Neural Networks: Creating a Model'
+challengeType: 11
+videoId: K8bz1bmOCTw
+dashedName: neural-networks-creating-a-model
+---
+
+# --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`
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d94
+title: 'Neural Networks: Optimizers'
+challengeType: 11
+videoId: hdOtRPQe1o4
+dashedName: neural-networks-optimizers
+---
+
+# --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.
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72d92
+title: Neural Networks with TensorFlow
+challengeType: 11
+videoId: uisdfrNrZW4
+dashedName: neural-networks-with-tensorflow
+---
+
+# --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.
+
+## --video-solution--
+
+3
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-example.md

@@ -0,0 +1,46 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da5
+title: 'Reinforcement Learning With Q-Learning: Example'
+challengeType: 11
+videoId: RBBSNta234s
+dashedName: reinforcement-learning-with-q-learning-example
+---
+
+# --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`
+
+## --video-solution--
+
+1
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md

@@ -0,0 +1,26 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da4
+title: 'Reinforcement Learning With Q-Learning: Part 2'
+challengeType: 11
+videoId: DX7hJuaUZ7o
+dashedName: reinforcement-learning-with-q-learning-part-2
+---
+
+# --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.
+
+## --video-solution--
+
+2
+

curriculum/challenges/espanol/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md

@@ -0,0 +1,30 @@
+---
+id: 5e8f2f13c4cdbe86b5c72da3
+title: Reinforcement Learning With Q-Learning
+challengeType: 11
+videoId: Cf7DSU0gVb4
+dashedName: reinforcement-learning-with-q-learning
+---
+
+# --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.
+
+## --video-solution--
+
+2
+