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.
Multi-Modal learning refers to AI models that learn from multiple sources of data, such as text, images, and audio. By incorporating information from multiple…