Post starts off kind of dense. Finally we get to the section "Word embedding models" and I say "ah ha! here we'll get a concise definition." Cut to ...

Naturally, every feed-forward neural network that takes words from a vocabulary as input and embeds them as vectors into a lower dimensional space, which it then fine-tunes through back-propagation, necessarily yields word embeddings as the weights of the first layer, which is usually referred to as Embedding Layer.

Naturally.

(Thanks, I believe that it is a great blog post, but I might look elsewhere for an intro ... :)