Tensors and Variables >>> What operations can be performed on tensors >>> Introduction to TensorFlow

1.

Question 1

What operations can be performed on tensors?

1 / 1 point

They can be sliced

Both A and B

None of the above.

=========================================

4.

Question 4

Which of the following produces tensors that can be modified?

1 / 1 point

tf.constant

tf.Variable

Both A and B

None of the above

=========================================

2.

Question 2

Which is an example of a rank 2 tensor?

1 / 1 point

Shape: [3,4]

Shape: [3,4,5]

Shape: [3]

Shape: [ ]

=========================================

5.

Question 5

What is the significance of tf.Variable()?

1 / 1 point

A tf.Variable represents a tensor whose value can be changed by running ops on it. Specific ops allow you to read and modify the values of this tensor. Higher level libraries like tf.keras use tf.Variable to store model parameters.

It is a general-purpose mathematical operations processing package.

It is a function used to define integer data.

None of the above

=========================================

3.

Question 3

Which of the following is true when we compute a loss gradient?

1 / 1 point

TensorFlow records all operations executed inside the context of a tf.GradientTape onto a tape.

It uses tape and the gradients associated with each recorded operation to compute the gradients.

The computed gradient of a recorded computation will be used in reverse mode differentiation.