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

@_dbrogle on Instagram: "https://arxiv.org/pdf/1301.3781 All LLMs use embeddings, and the concept of putting words into lists of numbers was so bad that it was unusable until Google did the classic throw more compute and training data at it, but also used two simple tasks to induce the AI into being properly aligned into encoding words into meaningful lists of numbers. The finer points of how they’re generated has since changed, but the fundamental concept of training a model to encode words int

Keyword - Meaning & Pronunciation Youtube --► https://www.youtube.com/@wordworld662/videos | Word World