Tensors

Test your understanding of tensors, their ranks, shapes, and roles in representing multidimensional data in machine learning.

FREE QUIZ

10 Questions

~20 Minutes

Intermediate

Question 1

What is a tensor in the context of machine learning?

A programming language A data preprocessing technique A multidimensional array used to store data A machine learning model

Question 2

Which of the following can be considered a tensor?

Scalars Vectors Matrices All of the above

Question 3

What does the 'rank' of a tensor refer to?

The number of data points The number of dimensions or axes The average value of elements The highest value in the tensor

Question 4

What is the shape of a 2D tensor with 2 rows and 4 columns?

[2, 4] [4, 2] [2, 2, 4] [2, 4, 2]

Question 5

A 3D tensor with shape [3, 2, 4] consists of:

3 scalars 3 vectors each of size 2 3 matrices of size 2x4 3 vectors of 4 scalars

Question 6

How many indices are needed to locate an element in a 4D tensor?

1 2 3 4

Question 7

Which tensor rank would best represent an RGB image with width, height, and color channels?

1 2 3 4

Question 8

What does the shape [2, 4, 2] tell us about the tensor?

It has 2 rows, 4 columns, and 2 layers It has 2 layers of 4x2 matrices It has 2 scalars in each axis It represents a 2D tensor

Question 9

Which of the following is TRUE about tensors?

They can only hold integer data They require at least 3 dimensions They generalize scalars, vectors, and matrices They are used only in image processing

Question 10

What is one major advantage of tensors in machine learning?

They reduce model size They simplify data types They efficiently represent high-dimensional data They eliminate the need for matrices

Back to Quizzes

0 / 10 Answered | 0:00