The inline links to pictures and other chapters are not rending on my machine. Perhaps I am missing an extension or dependency?
I know they advise running the notebooks in Kaggle or Google collab but so far it’s working fine on my machine (except for this). I also want to run whatever I can locally to learn the limitations of my GPU. I already had CUDA drivers working before starting this course because I have been horsing around with publicly-available projects like Whisper, Stable Diffusion, etc…
Is there a notebook extension that I might be missing? I do have nbextensions installed, so if the hyperlinks are rendered by nbextensions, then maybe I need to enable some extension that is currently unchecked.
Example from the 02_production.ipynb notebook:
Here is what the cell code looks like (sorry, I’m not sure how to make it wrap lines in markdown code blocks):
The six lines of code we saw in <<chapter_intro>> are just one small part of the process of using deep learning in practice. In this chapter, we're going to use a computer vision example to look at the end-to-end process of creating a deep learning application. More specifically, we're going to build a bear classifier! In the process, we'll discuss the capabilities and constraints of deep learning, explore how to create datasets, look at possible gotchas when using deep learning in practice, and more. Many of the key points will apply equally well to other deep learning problems, such as those in <<chapter_intro>>. If you work through a problem similar in key respects to our example problems, we expect you to get excellent results with little code, quickly.
Let's start with how you should frame your problem.
Thanks for any help!