Word2Vec
Word2Vec is a family of shallow neural models that learns vector representations of words from their contexts in text.
Natural Language Processing in Action, Second Edition
Word2Vec is a family of neural language models that learns dense vector representations of words from nearby-word prediction in text.
- How it is used
- Uses nearby-word prediction to learn vectors for similarity searches and vector-arithmetic analogies from unlabeled text.
- How deep it goes
- Works through vocabulary mapping, sliding-window pairs, a PyTorch prediction model, and gensim training parameters; leaves sentence-boundary detection optional.
- Examples
- Works through WikiText2 training pairs, a PyTorch embedding model, gensim similarity queries, and city–soccer-team analogies.
- What you can do after
- Build it from scratch in PyTorch or apply it via gensim to train domain-specific vectors and query similar words.
Read how Natural Language Processing in Action, Second Edition explains Word2Vec →
Deep Learning with R, Third Edition
word2vec is a family of shallow neural models that learn vector representations of words from local context relationships.
- How it is used
- Connects word co-occurrence patterns to distances in a pretrained embedding space.
- How deep it goes
- Explains the embedding principle, similarity measure, emergent relationships, and contrast with Transformers without detailing training implementation.
- Examples
- Uses king–queen vector arithmetic and directions for gender, plurality, and wild-animal-to-pet relationships.
- What you can do after
- Evaluate how co-occurrence-trained vectors encode similarity and semantic relationships.
Read how Deep Learning with R, Third Edition explains Word2Vec →
Data Without Labels
Word2Vec is a family of neural language models that learns vector representations of words from their surrounding context in a text corpus.
- How it is used
- Represents words for unsupervised text analysis when contextual meaning and relationships matter more than isolated word identity.
- How deep it goes
- Explains embeddings, co-occurrence, CBOW, and skip-gram, then defers the mathematical foundation while supporting Python implementation.
- Examples
- Uses the men–women and king–queen analogy to illustrate relational structure in vector space.
- What you can do after
- Evaluate/understand contextual word representations and develop Word2Vec models with Python.
Related concepts
- embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- GloVecontrasts with · GloVe is a word-embedding method that learns vector representations from global word-co-occurrence statistics across a corpus.
- skip-gramuses · Skip-gram is a word-embedding training architecture that learns to predict surrounding context items from a target item.
- backpropagationuses · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
- distributional hypothesisimplements · The distributional hypothesis is the linguistic principle that words or phrases appearing in similar contexts tend to have related meanings.
- gensimused by · Gensim is an open-source Python library for unsupervised natural-language processing, including topic modeling, document similarity, and distributed representations.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.