Feedback from Morgan and Diane

[msschwartz21]

• Create an exercise and solution notebook on the main branch instead of split into different branches
• Add blue blocks to tasks with the title within the color block
• Add learning goals to checkpoints
• Task 1: Add more hints about pytorch layers and links to the documentation
• In task the training loop, explain why a special weights initialization is used
• Explain the confusion matrix or reference a previous exercise (appears in intro to ml)
• Task 2: add locations in code where they need to be making changes (e.g. as comments)
• Could we just use one of the existing models to do this instead of re-training something? (e.g. lean into how the “clean” model above does on “tainted” data, which is the same thing).
• For the last checkpoint, more the questions before the checkpoint.

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Universal tasks

• Update environment setup to use mamba (See https://github.com/dlmbl/DL-MBL-2023/wiki/Exercise-Guidelines#environment)
• Add a red callout block to the beginning of each notebook that specifies which kernel to use

<div class="alert alert-danger">
Please use kernel <kernel name here>
</div>

• Check for any references to slack. We are going to use Element this year.