Embeddings
Embeddings are a machine learning technique that maps discrete, high-dimensional data (like words, images, or categories) into continuous, low-dimensional vector spaces. They capture semantic relationships and similarities, enabling algorithms to process and compare complex data efficiently. This is foundational in natural language processing, recommendation systems, and computer vision.
Developers should learn embeddings when working with unstructured data like text, images, or user interactions, as they enable tasks such as semantic search, similarity matching, and feature representation in models. They are essential for building applications like chatbots, content recommendations, and anomaly detection, where understanding context and relationships is critical.