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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Coursera Exercise Quiz Answers

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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Coursera Exercise Quiz Answers

Enroll Here: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 1 Quiz Answers: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Question 1: The diagram for traditional programming had Rules and Data In, but what came out?

  • Machine Learning
  • Bugs
  • Answers
  • Binary

Question 2: The diagram for Machine Learning had Answers and Data In, but what came out?

  • Bugs
  • Models
  • Rules
  • Binary

Question 3: When I tell a computer what the data represents (i.e. this data is for walking, this data is for running), what is that process called?

  • Programming the Data
  • Categorizing the Data
  • Learning the Data
  • Labelling the Data

Question 4: What is a Dense?

  • A single neuron
  • A layer of disconnected neurons
  • A layer of connected neurons
  • Mass over Volume

Question 5: What does a Loss function do?

  • Measures how good the current ‘guess’ is
  • Decides to stop training a neural network
  • Figures out if you win or lose
  • Generates a guess

Question 6: What does the optimizer do?

  • Figures out how to efficiently compile your code
  • Generates a new and improved guess
  • Decides to stop training a neural network
  • Measures how good the current guess is

Question 7: What is Convergence?

  • A dramatic increase in loss
  • The process of getting very close to the correct answer
  • A programming API for AI
  • The bad guys in the next ‘Star Wars’ movie

Question 8: What does model.fit do?

  • It optimizes an existing model
  • It determines if your activity is good for your body
  • It makes a model fit available memory
  • It trains the neural network to fit one set of values to another

Download Week 1 Exercise Solutions: Programming Assignment: Exercise 1 (Housing Prices) Solved


Week 2 Quiz Answers: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Question 1: What’s the name of the dataset of Fashion images used in this week’s code?

  • Fashion MNIST
  • Fashion Data
  • Fashion MN
  • Fashion Tensors

Question 2: What do the above mentioned Images look like?

  • 28×28 Greyscale
  • 28×28 Color
  • 82×82 Greyscale
  • 100×100 Color

Question 3: How many images are in the Fashion MNIST dataset?

  • 10,000
  • 42
  • 70,000
  • 60,000

Question 4: Why are there 10 output neurons?

  • Purely arbitrary
  • To make it train 10x faster
  • There are 10 different labels
  • To make it classify 10x faster

Question 5: What does Relu do?

  • It only returns x if x is less than zero
  • It returns the negative of x
  • For a value x, it returns 1/x
  • It only returns x if x is greater than zero

Question 6: Why do you split data into training and test sets?

  • To train a network with previously unseen data
  • To make training quicker
  • To test a network with previously unseen data
  • To make testing quicker

Question 7: What method gets called when an epoch finishes?

  • On_training_complete
  • on_end
  • on_epoch_finished
  • on_epoch_end

Question 8: What parameter to you set in your fit function to tell it to use callbacks?

  • callback=
  • oncallback=
  • callbacks=
  • oncallbacks=

Download Week 2 Exercise Solution – Programming Assignment: Exercise 2 Solved


Week 3 Quiz Answers: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Question 1: What is a Convolution?

  • A technique to make images smaller
  • A technique to make images bigger
  • A technique to isolate features in images
  • A technique to filter out unwanted images

Question 2: What is a Pooling?

  • A technique to combine pictures
  • A technique to make images sharper
  • A technique to isolate features in images
  • A technique to reduce the information in an image while maintaining features

Question 3: How do Convolutions improve image recognition?

  • They make processing of images faster
  • They isolate features in images
  • They make the image clearer
  • They make the image smaller

Question 4: After passing a 3×3 filter over a 28×28 image, how big will the output be?

  • 26×26
  • 28×28
  • 25×25
  • 31×31

Question 5: After max pooling a 26×26 image with a 2×2 filter, how big will the output be?

  • 13×13
  • 56×56
  • 26×26
  • 28×28

Question 6: Applying Convolutions on top of our Deep neural network will make training:

  • Slower
  • It depends on many factors. It might make your training faster or slower, and a poorly designed Convolutional layer may even be less efficient than a plain DNN!
  • Stay the same
  • Faster

Download Week 3 Exercise Solution: Programming Assignment: Exercise 3 (Improve MNIST with convolutions))


Week 4 Quiz Answers: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Question 1: Using Image Generator, how do you label images?

  • It’s based on the directory the image is contained in
  • It’s based on the file name
  • TensorFlow figures it out from the contents
  • You have to manually do it

Question 2: What method on the Image Generator is used to normalize the image?

  • normalize_image
  • rescale
  • normalize
  • Rescale_image

Question 3: How did we specify the training size for the images?

  • The target_size parameter on the validation generator
  • The training_size parameter on the training generator
  • The training_size parameter on the validation generator
  • The target_size parameter on the training generator

Question 4: When we specify the input_shape to be (300, 300, 3), what does that mean?

  • There will be 300 images, each size 300, loaded in batches of 3
  • Every Image will be 300×300 pixels, with 3 bytes to define color
  • There will be 300 horses and 300 humans, loaded in batches of 3
  • Every Image will be 300×300 pixels, and there should be 3 Convolutional Layers

Question 5: If your training data is close to 1.000 accuracy, but your validation data isn’t, what’s the risk here?

  • No risk, that’s a great result
  • You’re overfitting on your training data
  • You’re underfitting on your validation data
  • You’re overfitting on your validation data

Question 6: Convolutional Neural Networks are better for classifying images like horses and humans because:

  • In these images, the features may be in different parts of the frame
  • There’s a wide variety of horses
  • There’s a wide variety of humans
  • All of the above

Question 7: After reducing the size of the images, the training results were different. Why?

  • There was less information in the images
  • There was more condensed information in the images
  • We removed some convolutions to handle the smaller images
  • The training was faster

Download Week 4 Exercise Solution: Programming Assignment: Exercise 4 (Handling complex images) Solved

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