[QUESTION]

I'm a bit confused about the **"Encoding Categorical Features Using Embeddings"** on Chapter 13,

There you provided an example about encoding a categorical data, and said that 50,000 one-hot encoded categories is equivalent to around 100 embedding dimensions...
But the sample you provided isn't clear enough to understand the comparison...

The code you provided just output an Tensor shape=(3, 2) of the categories, and what else? (I expected a complete Model that outputs some real training data with compile and fit, so we can see the difference).

Can you provide a link to a more clear comparison between one-hot encoding and Embeddings?
Where should I use one or the other?

Thanks for sharing your knowledge with 3º version of the book, very impressive!