Conclusion

Embeddings turn discrete items into trainable vectors.

You have seen the core pieces:

• a lookup table selects a row
• an embedding matrix stores one vector per item
• categorical ids act as addresses
• learned vectors create representation geometry
• similarity becomes meaningful after training
• embedding rows are parameters
• token ids can enter neural networks through embeddings

This chapter is the bridge from general neural networks to language modeling. The next Deep Learning chapter briefly studies convolution, because convolution, locality, feature maps, and parameter sharing are part of the vocabulary many modern architectures assume.