Lab 1: Warmup | CS 375-376 Spring 2026 at Calvin University

Objectives

Use a Jupyter notebook to run Python code
Start to uncover how an image classifier works.

Step 0: Log in to your Google account

You can use either your personal account (if you have one) or your Calvin account.

Note: I don’t recommend trying to run this on your own computer at this point; even if you have a compatible GPU, getting Python to work with it can be a project.

Step 1: Jupyter Notebooks

In this section, we’ll practice working with Jupyter notebooks. You may find these references helpful:

Colab overview
some parts of the Jupyter Notebooks docs

A number will appear next to each of the code cells when they have run successfully.

Add a code cell that computes 1+1. Check the output.

Note carefully the difference between Command mode and Edit mode.

I highly encourage you to get comfortable with keyboard shortcuts for the following operations:

Switch between edit and command mode. (Enter/Return and Esc)
Insert a cell above or below (a and b)
Change a cell to code / Markdown (m or y in Command mode)
Run the current cell (with or without selecting the cell below) (Ctrl-Enter or Shift-Enter)

For more keyboard shortcuts click the Command Palette button on the bottom toolbar on Kaggle (it may be hidden by a cookie consent bar!) or use a search engine.

Suppose a cell has multiple expressions in it, like this:

x = 1.0
x * 2
print("Something")
x = x * 2
x * 2

Which one gets displayed? Can you figure out the general rule for what output gets displayed from a notebook cell?

Add a Markdown cell with your answer to this question.

When you’re done, save your notebook and submit it on Moodle.

Step 2: Image Classifier

In the next section, you’ll work with a basic image classifier.

In this section (and most future Labs), the tasks to do are inside the notebook itself. You’ll find cells labeled Task and blank code chunks usually labeled # your code here. Follow the instructions top-to-bottom, then download and submit when done.

Checklist:

Did we fill in the table with the accuracy and loss numbers?
Did we think about whether the predictions are a probability distribution?
Did we try out using argmax? Can we get it to show the name of the predicted class, not just its number?
Did we try changing something to see what effect it has on the accuracy?