Word Embeddings are a technique in NLP that represent words as continuous vectors in a high-dimensional space. These vectors capture semantic and syntactic relationships between words. Word embeddings are useful for tasks such as language translation, sentiment analysis, and document clustering.
Deep Learning, a subfield of AI, leverages neural networks with numerous interconnected layers to process vast amounts of data, enabling machines to learn and…