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 Reinforcement Learning is a subset of Machine Learning that combines Deep Learning and Reinforcement Learning. It involves training AI models to make decisions…