1. Start a new TAP session as documented in Lab Setup
2. If you have not done so, download the notebooks as instructed in Lab Setup
3. Navigate to the sciml-course/cnn directory, and open cnn_part2.ipynb
4. Inside the notebook, under the Kernel dropdown menu, click on Change Kernel and choose cnn_course_kernel
5. Now that the notebook is set up to run the new kernel, you can either step through each cell of the notebook one-at-a-time by repeatedly clicking on the Run button near the top, or you can select Run All from the Cell dropdown menu to run through all the cells sequentially

If you are running the notebook and encounter a ModuleNotFoundError error, double check that you have changed the kernel to cnn_course_kernel. If the kernel is changed correctly, it should display cnn_course_container on the top right.

This notebook will use the same hyperparameters as used in part 1:

• Learning Rate (lr): how much model parameters are updated at each batch/epoch
• Batch Size: number of data points used to estimate gradients at each iteration
• Epochs: Number of times to iterate over our entire dataset in optimization process

These hyperparameters will be used throughout the notebook.