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.
