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

All Weeks Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

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

Week 01: Quiz Anwers

Q1. The diagram for traditional programming had Rules and Data In, but what came out?

  • came out?
  • Machine Learning
  • Bugs
  • Answers
  • Binary

Q2. The diagram for Machine Learning had Answers and Data In, but what came

  • out?
  • Bugs
  • Models
  • Rules
  • Binary

Q3. 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

Q4. What is a Dense?

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

Q5. 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

Q6. 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

Q7. 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

Q8. 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 Assignments Solutions

Week 02: Quiz Anwers

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

Fashion MNIST
Fashion Data
Fashion Tensors

Q2. What do the above-mentioned Images look like?

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

Q3. How many images are in the Fashion MNIST dataset?

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

Q4. 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


Q5. 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

Q6. 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

Q7.What methods gets called when a epoch finishes?

  • on_training_complete
  • on_end
  • on_epoch_finished
  • on_epoch_end

Q8. What parameter to you set in your fit function to tell it to use callbacks?

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

Download Week 2: Exercise Assignments Solutions

Week 03: Quiz Anwers

Q1. 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

Q2. 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

Q3. 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

Q4. 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

Q5. 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

Q6. 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 Assignments Solutions

Week 04: Quiz Anwers

Q1. 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

Q2. What method on the Image Generator is used to normalize the image?

  • normalize_image
  • rescale
  • normalize
  • Rescale_image

Q3. 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

Q4. 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

Q5. 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

Q6. 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

Q7. 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
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Coursera Course Review:

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Convolutional Neural Networks in TensorFlow

All Course Quiz Answers of DeepLearning.AI TensorFlow Developer Professional Certificate

Course 01: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Course 02: Convolutional Neural Networks in TensorFlow

Course 03: Natural Language Processing in TensorFlow

Course 04: Sequences, Time Series, and Prediction

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