veekaybee/what_are_embeddings
A deep dive into embeddings starting from fundamentals
Comprehensive survey covering embeddings' evolution from TF-IDF and PCA through Word2Vec to modern Transformers, with practical industry usage patterns. Includes LaTeX source document compiled via GitHub Actions, accompanying Jupyter notebooks demonstrating concepts, and a generated website. Targets ML practitioners seeking both theoretical foundations and real-world implementation guidance across traditional statistical and neural embedding approaches.
1,060 stars.
Stars
1,060
Forks
86
Language
Jupyter Notebook
License
—
Category
Last pushed
Jan 17, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/veekaybee/what_are_embeddings"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cosmosgl/graph
GPU-accelerated force graph layout and rendering
Clay-foundation/model
The Clay Foundation Model - An open source AI model and interface for Earth
nomic-ai/nomic
Nomic Developer API SDK
alexshtf/torchcurves
Parametric differentiable curves with PyTorch for continuous embeddings, shape-restricted models, or KANs
omoindrot/tensorflow-triplet-loss
Implementation of triplet loss in TensorFlow