Hands-On Transfer Learning with Python

Word2vec model

This model was created by Google in 2013 and is a predictive, deep learning-based model that computes and generates high quality, distributed, and continuous dense vector representations of words, which capture contextual and semantic similarity. Essentially, these are unsupervised models that can take in massive textual corpora, create a vocabulary of possible words, and generate dense word embeddings for each word in the vector space representing that vocabulary. Usually, you can specify the size of the word embedding vectors, and the total number of vectors is essentially the size of the vocabulary. This makes the dimensionality of this dense vector space much lower than the high-dimensional sparse vector space built using ...