You've learned about the core mathematical engine of machine learning: elementwise operations, broadcasting, and reductions. Let's practice applying these fundamental calculations.

Engagement Message

Ready to compute?

Type

Multiple Choice

Practice Question

What is the result of the following broadcasting operation?

A. tensor([[11, 2], [3, 4]]) B. tensor([[11, 12], [13, 14]]) C. tensor([11, 12, 13, 14]) D. This will cause an error.

Suggested Answers

• A
• B - Correct
• C
• D

Type

Fill In The Blanks

Markdown With Blanks

Fill in the blank to compute the sum of each column in the tensor.

Suggested Answers

• 0
• 1
• None

Type

Sort Into Boxes

Practice Question

Sort these operations into the correct category.

Labels

• First Box Label: Elementwise Op
• Second Box Label: Reduction Op

First Box Items

• Addition (+)
• Multiplication (*)

Second Box Items

• torch.mean()
• torch.max()
• torch.sum()

Type

Swipe Left or Right

Practice Question

Does the scenario describe broadcasting? Swipe left for Yes, right for No.

Labels

• Left Label: Broadcasting
• Right Label: Not Broadcasting

Left Label Items

• Adding a matrix (3,4) and a vector (4,)
• Multiplying a matrix (2,2) and a scalar
• Adding a matrix (3,4) and a vector (3,1)

Right Label Items

• Adding two matrices of the same shape (2,2)
• Multiplying a vector (5,) and another vector (5,)

Type

Multiple Choice

Practice Question

To normalize a feature column in a dataset (a 2D tensor), you first calculate the mean of that column. Which operation would you use to get the mean of the second column (index=1)?

A. torch.mean(data) B. torch.mean(data, dim=1) C. torch.mean(data[:, 1].float()) D. torch.mean(data[1, :])

Suggested Answers

• A
• B
• C - Correct
• D