Insightforge | AI & Data Science on Instagram: "Word embeddings represent words as vectors in a high-dimensional space, where each word is assigned a numerical vector. The key idea is that words with similar meanings end up close to one another in this space, allowing models to recognize relationships like synonyms, similarities, and even analogies. A major strength of embeddings is how they use position and direction within the vector space to encode semantic meaning. While simple embeddings ca

Create a Blackboard Test from a Word Document

Microsoft Word Test | Skills Assessment - TestGorilla