A guide on how to use OpenAI embeddings and clustering techniques to analyze survey data and extract meaningful topics and actionable insights from the responses.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
A map of math articles from ArXiv using t-SNE and nomic-embed.
A step-by-step guide on understanding and implementing t-SNE for visualizing high-dimensional data using Python.
Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques
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