Word embeddings are a foundational concept in Natural Language Processing (NLP), enabling models to represent words as dense vectors in high-dimensional space. These vectors capture semantic relationships such as similarity,….
Transformers have revolutionized natural language processing and other domains by enabling highly parallel, scalable models that capture long-range dependencies in data. They are the backbone of large language models (LLMs)….
In the classical world, data compression is everywhere. From zip files to video streaming, compression helps us store and transmit more with less. The idea is simple—remove redundancy and represent….
