This article demonstrates how to construct a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based algorithm. By transforming raw, unstructured text into semantic mathematical representations, you can automatically discover hidden topics in unlabeled datasets without requiring prior labels.
Key stages of the process include:
* Generating text embeddings using pre-trained sentence-transformers models
* Reducing embedding dimensionality with UMAP to prepare data for clustering
* Applying HDBSCAN to identify clusters and visualize results through scatterplots
Key stages of the process include:
* Generating text embeddings using pre-trained sentence-transformers models
* Reducing embedding dimensionality with UMAP to prepare data for clustering
* Applying HDBSCAN to identify clusters and visualize results through scatterplots
A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
