Hi Chris,
I’m just trying to make the connection between nn.Embedding and nn.Linear. I think I understand what an embedding is: A representation of the input in a different vector space. Would you mind clarifying this point:

Now giving such a vector v with v[2]=1 (cf. example vector above) to the Linear layer gives you simply the 2nd row of that layer.

I think I’ve made embeddings before by training an LSTM autoencoder to reconstruct the input sequence from the final hidden state. I thought the embedding would then be the hidden state of the encoder.
I’ve only ever done time-series related work with LSTMs, but am trying to learn their NLP applications and can’t find a word written on NLP that doesn’t include the use of an nn.Embedding layer.