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.
Modular Neural Networks are AI models composed of smaller interconnected modules, each responsible for a specific sub-task or component. These modular architectures allow for…