veekaybee/what_are_embeddings
An educational survey paper and notebook collection explaining embeddings from fundamentals through modern transformer-based approaches.
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This repository contains a comprehensive survey paper, LaTeX document, and Jupyter notebooks that explain embeddings as a core ML data structure. It covers the evolution from TF-IDF and one-hot encoding through Word2Vec to transformer architectures and transfer learning. The materials explore how embeddings enable semantic understanding and scaling in modern machine learning systems, with practical industry usage patterns.
Frequently asked
- What is veekaybee/what_are_embeddings?
- An educational survey paper and notebook collection explaining embeddings from fundamentals through modern transformer-based approaches.
- Is what_are_embeddings open source?
- Yes — veekaybee/what_are_embeddings is an open-source project tracked on heatdrop.
- What language is what_are_embeddings written in?
- veekaybee/what_are_embeddings is primarily written in Jupyter Notebook.
- How popular is what_are_embeddings?
- veekaybee/what_are_embeddings has 1.1k stars on GitHub.
- Where can I find what_are_embeddings?
- veekaybee/what_are_embeddings is on GitHub at https://github.com/veekaybee/what_are_embeddings.