Question 1

In supervised deep learning, we aim to predict the ________ labels supplied with data samples.

Accepted Answer

A)  goal 
B)  mark 
C)  object 
D)  target 
A)  goal 
B)  mark 
C)  object 
D)  target

Question 2

The IPython magic ________ indicates that Matplotlib-based graphics should be displayed in a Jupyter notebook rather than in separate windows.

Accepted Answer

A)  %matplotlib notebook 
B)  %matplotlib inline 
C)  xe "magics:%matplotlib inline"xe "%matplotlib inline magic[matplotlib inline magic]"%matplotlib jupyter 
D)  None of the above 
A)  %matplotlib notebook 
B)  %matplotlib inline 
C)  xe "magics:%matplotlib inline"xe "%matplotlib inline magic[matplotlib inline magic]"%matplotlib jupyter 
D)  None of the above

Question 3

Which of the following deep-learning demos translates a line drawing into a picture:

Accepted Answer

A)  DeepArt.io. 
B)  DeepWarp Demo. 
C)  Image-to-Image Demo. 
D)  Google Translate Mobile App. 
A)  DeepArt.io. 
B)  DeepWarp Demo. 
C)  Image-to-Image Demo. 
D)  Google Translate Mobile App.

Question 4

A Keras model's ________ method returns the loss and accuracy values of a trained model.

Accepted Answer

A)  assess 
B)  account 
C)  grade 
D)  evaluate 
A)  assess 
B)  account 
C)  grade 
D)  evaluate

Question 5

You train a Keras model by calling its fit method. Which of the following statements about the fit method is false?

Accepted Answer

A)  As in Scikit-learn, the first two arguments are the training data and the categorical target labels. 
B)  The iterations argument specifies the number of times the model should process the entire set of training data. 
C)  batch_size specifies the number of samples to process at a time during each epoch. Most models specify a power of 2 from 32 to 512. Larger batch sizes can decrease model accuracy. 
D)  In general, some samples should be used to validate the model. If you specify validation data, after each epoch, the model will use it to make predictions and display the validation loss and accuracy. You can study these values to tune your layers and the fit method's hyperparameters, or possibly change the layer composition of your model. 
A)  As in Scikit-learn, the first two arguments are the training data and the categorical target labels. 
B)  The iterations argument specifies the number of times the model should process the entire set of training data. 
C)  batch_size specifies the number of samples to process at a time during each epoch. Most models specify a power of 2 from 32 to 512. Larger batch sizes can decrease model accuracy. 
D)  In general, some samples should be used to validate the model. If you specify validation data, after each epoch, the model will use it to make predictions and display the validation loss and accuracy. You can study these values to tune your layers and the fit method's hyperparameters, or possibly change the layer composition of your model.

Question 6

Which of the following statements is false?

Accepted Answer

A)  Convnets often have many convolution and pooling layers. 
B)  The Keras team's convnets tend to double the number of filters in subsequent convolutional layers to enable the model to learn more relationships between the features. 
C)  The following snippets add a convolution layer with 128 filters, followed by a pooling layer to reduce the dimensionality by 50%:
Cnn)add(Conv2D(filters=128, kernel_size=(3, 3),
Activation='relu'))
Cnn)add(MaxPooling2D(pool_size=(2, 2))) 
D)  For odd dimensions like 11-by-11, Keras pooling layers round the dimensions down by default. 
A)  Convnets often have many convolution and pooling layers. 
B)  The Keras team's convnets tend to double the number of filters in subsequent convolutional layers to enable the model to learn more relationships between the features. 
C)  The following snippets add a convolution layer with 128 filters, followed by a pooling layer to reduce the dimensionality by 50%:
Cnn)add(Conv2D(filters=128, kernel_size=(3, 3),
Activation='relu'))
Cnn)add(MaxPooling2D(pool_size=(2, 2))) 
D)  For odd dimensions like 11-by-11, Keras pooling layers round the dimensions down by default.

Question 7

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The following code begins configuring a xe "convolutional neural network (CNN or convnet):model"convolutional neural network with a Keras Sequential model from the tensorflow.keras.models module:
[24]: from tensorflow.keras.models import Sequential
[25]: cnn = Sequential() 
B)  The network resulting from the code in Part (a) will execute its layers sequentially-the output of one layer becomes the input to the next. 
C)  Networks that operate as described in Part (b) are called feed-forward networks. All neural networks operate this way. 
D)  All of the above statements are true. 
A)  The following code begins configuring a xe "convolutional neural network (CNN or convnet):model"convolutional neural network with a Keras Sequential model from the tensorflow.keras.models module:
[24]: from tensorflow.keras.models import Sequential
[25]: cnn = Sequential() 
B)  The network resulting from the code in Part (a) will execute its layers sequentially-the output of one layer becomes the input to the next. 
C)  Networks that operate as described in Part (b) are called feed-forward networks. All neural networks operate this way. 
D)  All of the above statements are true.

Question 8

Which of the following statements about a convolutional layer a), b) or c) is false?

Accepted Answer

A)  The areas that a convolutional layer learns from are called kernels. 
B)  For a 3-by-3 kernel, when the kernel finishes moving left-to-right and reaches the right edge of an image, the convolutional layer moves the kernel down three pixels and repeats this left-to-right process. 
C)  Kernels typically are 3-by-3, though larger kernels can be used for higher-resolution images. 
D)  All of the above statements are true. 
A)  The areas that a convolutional layer learns from are called kernels. 
B)  For a 3-by-3 kernel, when the kernel finishes moving left-to-right and reaches the right edge of an image, the convolutional layer moves the kernel down three pixels and repeats this left-to-right process. 
C)  Kernels typically are 3-by-3, though larger kernels can be used for higher-resolution images. 
D)  All of the above statements are true.

Question 9

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Neural network models can require significant training time. Once you've designed and tested a model that suits your needs, you can save its state. This allows you to load it later to make more predictions. Sometimes models are loaded and further trained for new problems. For example, layers in our model already know how to recognize features such as lines and curves, which could be useful in handwritten character recognition as well. This process is called transfer learning-you transfer an existing model's knowledge into a new model. 
B)  A Keras model's save method stores the model's architecture and state information in a format called Hierarchical Data Format (HDF5). Such files use the .xe "h5 file extension for Hierarchical Data Format files"h5 file extension. 
C)  You can load a saved model with the load_model function from the tensorflow.keras.models module, as in: from tensorflow.keras.models import load_model
Cnn = load_model('mnist_cnn.h5')
You can then invoke the loaded model's methods. For example, if you've acquired more data, you could call the model's predict method to make additional predictions on new data, or you could call the model's fit method to start training with the additional data. 
D)  All of the above statements are true. 
A)  Neural network models can require significant training time. Once you've designed and tested a model that suits your needs, you can save its state. This allows you to load it later to make more predictions. Sometimes models are loaded and further trained for new problems. For example, layers in our model already know how to recognize features such as lines and curves, which could be useful in handwritten character recognition as well. This process is called transfer learning-you transfer an existing model's knowledge into a new model. 
B)  A Keras model's save method stores the model's architecture and state information in a format called Hierarchical Data Format (HDF5). Such files use the .xe "h5 file extension for Hierarchical Data Format files"h5 file extension. 
C)  You can load a saved model with the load_model function from the tensorflow.keras.models module, as in: from tensorflow.keras.models import load_model
Cnn = load_model('mnist_cnn.h5')
You can then invoke the loaded model's methods. For example, if you've acquired more data, you could call the model's predict method to make additional predictions on new data, or you could call the model's fit method to start training with the additional data. 
D)  All of the above statements are true.

Question 10

Which of the following statements is false?

Accepted Answer

A)  Keras offers a friendly interface to Google's TensorFlow-the most widely used deep-learning library. 
B)  François Chollet of the Google Mind team developed Keras to make deep-learning capabilities more accessible. 
C)  Keras enables you to define deep-learning models conveniently with one statement. 
D)  Google has thousands of TensorFlow and Keras projects underway internally, and that number is growing quickly. 
A)  Keras offers a friendly interface to Google's TensorFlow-the most widely used deep-learning library. 
B)  François Chollet of the Google Mind team developed Keras to make deep-learning capabilities more accessible. 
C)  Keras enables you to define deep-learning models conveniently with one statement. 
D)  Google has thousands of TensorFlow and Keras projects underway internally, and that number is growing quickly.

Question 11

Which of the following Keras datasets for practicing deep learning is used for sentiment analysis?

Accepted Answer

A)  MNIST. 
B)  Fashion-MNIST. 
C)  IMDb. 
D)  CIFAR10. 
A)  MNIST. 
B)  Fashion-MNIST. 
C)  IMDb. 
D)  CIFAR10.

Question 12

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  During training, the error rate is known as the loss, and the calculation that determines the loss is called the loss function. 
B)  Throughout training, the network determines the amount that each neuron contributes to the overall loss, then goes back through the layers and adjusts the weights in an effort to minimize that loss. 
C)  The technique mentioned in Part (b) is called backpropagation. Optimizing these weights occurs gradually-typically via a process called gradient descent. 
D)  All of the above statements are true. 
A)  During training, the error rate is known as the loss, and the calculation that determines the loss is called the loss function. 
B)  Throughout training, the network determines the amount that each neuron contributes to the overall loss, then goes back through the layers and adjusts the weights in an effort to minimize that loss. 
C)  The technique mentioned in Part (b) is called backpropagation. Optimizing these weights occurs gradually-typically via a process called gradient descent. 
D)  All of the above statements are true.

Question 13

Which of the following statements is false?

Accepted Answer

A)  Overfitting can occur when your model is too simple compared to what it is modeling-in the most extreme overfitting case, a model memorizes its training data. 
B)  When you make predictions with an overfit model, they will be accurate if new data matches the training data, but the model could perform poorly with data it has never seen. 
C)  Overfitting tends to occur in deep learning as the dimensionality of the layers becomes too large. 
D)  Some techniques to prevent overfitting include training for fewer epochs, xe "data augmentation"data augmentation, xe "dropout"dropout and xe "L1 regularization"L1 or xe "regularization"xe "L2 regularization"L2 regularization. 
A)  Overfitting can occur when your model is too simple compared to what it is modeling-in the most extreme overfitting case, a model memorizes its training data. 
B)  When you make predictions with an overfit model, they will be accurate if new data matches the training data, but the model could perform poorly with data it has never seen. 
C)  Overfitting tends to occur in deep learning as the dimensionality of the layers becomes too large. 
D)  Some techniques to prevent overfitting include training for fewer epochs, xe "data augmentation"data augmentation, xe "dropout"dropout and xe "L1 regularization"L1 or xe "regularization"xe "L2 regularization"L2 regularization.

Question 14

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The one-hot encoded representation of the digit 7 is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] 
B)  The tensorflow.keras.utils module provides function to_categorical to perform xe "one-hot encoding[one hot encoding]"one-hot encoding. 
C)  The to_categorical function counts the unique categories then, for each item being encoded, creates an array of that length with a 1.0 in the correct position. 
D)  All of the above statements are true. 
A)  The one-hot encoded representation of the digit 7 is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] 
B)  The tensorflow.keras.utils module provides function to_categorical to perform xe "one-hot encoding[one hot encoding]"one-hot encoding. 
C)  The to_categorical function counts the unique categories then, for each item being encoded, creates an array of that length with a 1.0 in the correct position. 
D)  All of the above statements are true.

Question 15

Consider the following code: import numpy as np
Index = np.random.choice(np.arange(len(X_train)), 24, replace=False)
Figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(16, 9))
For item in zip(axes.ravel(), X_train[index], y_train[index]):
Axes, image, target = item
Axes.imshow(image, cmap=plt.cm.gray_r)
Axes.set_xticks([]) # remove x-axis tick marks
Axes.set_yticks([]) # remove y-axis tick marks
Axes.set_title(target)
Plt)tight_layout()
Which of the following statements a), b) or c) is false?

Accepted Answer

A)  NumPy's choice function (from the numpy.random module) selects the number of elements specified in its second argument from the front of the array of values in its first. 
B)  The choice function returns an array containing the selected values, which we store in index. 
C)  The expressions X_train[index] and y_train[index] use index to get the corresponding elements from both arrays. 
D)  All of the above statements are true. 
A)  NumPy's choice function (from the numpy.random module) selects the number of elements specified in its second argument from the front of the array of values in its first. 
B)  The choice function returns an array containing the selected values, which we store in index. 
C)  The expressions X_train[index] and y_train[index] use index to get the corresponding elements from both arrays. 
D)  All of the above statements are true.

Question 16

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Because IMDb movie reviews are numerically encoded in the dataset bundled with Keras, to view their original text, you need to know the word to which each number corresponds. 
B)  Keras's IMDb dataset provides a dictionary that maps the words to their indexes. Each word's corresponding value is its frequency ranking among all the words in the entire set of reviews. 
C)  In the dictionary mentioned in Part (b), the word with the ranking 1 is the most frequently occurring word (calculated by the Keras team from the dataset), the word with ranking 2 is the second most frequently occurring word, and so on. Though the dictionary values begin with 1 as the most frequently occurring word, in each encoded review, the ranking values are offset by 3. So any review containing the most frequently occurring word will have the value 4 wherever that word appears in the review. 
D)  All of the above statements are true. 
A)  Because IMDb movie reviews are numerically encoded in the dataset bundled with Keras, to view their original text, you need to know the word to which each number corresponds. 
B)  Keras's IMDb dataset provides a dictionary that maps the words to their indexes. Each word's corresponding value is its frequency ranking among all the words in the entire set of reviews. 
C)  In the dictionary mentioned in Part (b), the word with the ranking 1 is the most frequently occurring word (calculated by the Keras team from the dataset), the word with ranking 2 is the second most frequently occurring word, and so on. Though the dictionary values begin with 1 as the most frequently occurring word, in each encoded review, the ranking values are offset by 3. So any review containing the most frequently occurring word will have the value 4 wherever that word appears in the review. 
D)  All of the above statements are true.

In supervised deep learning, we aim to predict the ________ labels supplied with data samples.

The IPython magic ________ indicates that Matplotlib-based graphics should be displayed in a Jupyter notebook rather than in separate windows.

Which of the following deep-learning demos translates a line drawing into a picture:

A Keras model's ________ method returns the loss and accuracy values of a trained model.

You train a Keras model by calling its fit method. Which of the following statements about the fit method is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements about a convolutional layer a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following Keras datasets for practicing deep learning is used for sentiment analysis?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Machine Learning: Classification, Regression and Clustering

Big Data: Hadoop, Spark, Nosql and Iot

Filters

Exam 16: Deep Learning

In supervised deep learning, we aim to predict the ________ labels supplied with data samples.

The IPython magic ________ indicates that Matplotlib-based graphics should be displayed in a Jupyter notebook rather than in separate windows.

Which of the following deep-learning demos translates a line drawing into a picture:

A Keras model's ________ method returns the loss and accuracy values of a trained model.

You train a Keras model by calling its fit method. Which of the following statements about the fit method is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements about a convolutional layer a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following Keras datasets for practicing deep learning is used for sentiment analysis?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Machine Learning: Classification, Regression and Clustering

Big Data: Hadoop, Spark, Nosql and Iot

Filters