Pretrained word embeddings vs model weights I'm trying to understand the relationship between pretrained word vectors and pretrained  weights when using pretrained word vectors in another neural network.

Are the vectors themselves the weights or do the weights need to be transferred in addition to selecting embedded representations? What's the high level conceptual process in using pretrained vectors? 

Thanks!
Pre-trained word embedding methods such as word2vec, are used in NLP for the initialization of the first layer of a neural network before training for a new task. It is a single layer of weights (known as embeddings) and any prior knowledge from the word embeddings is only present in the first layer of the network, so the entire network would still need to be trained for a new target task.