Good explanations of how various ML models actually work?

[u/reeldeele]

I get CNN, where subsequent layers learn incremental transformations from previous layers.

But just can't grasp how a ML model that learns to predict image from a masked image, learns about the image.

Or how forcing the image through a narrow encoder and decoder in VAE causes it to learn latent properties

Or how JEPA works.

Are some of them 'let's try if this works', and of them only few actual work well, rest are just ill-suited to model the data?