This is my Day 2 of learning AI fundamentals where I will be covering the following concepts: Vector Embeddings How Tokenisation and Vector Embeddings relate to each other Vector embeddings: Vector embeddings is the process of turning each token id(generated during tokenisation) into high dimensional vector where semantic similarity results into geometric closeness. Think of it like this: dog is closer to puppy, also closer to dog food. But dog is not closer to car or petrol. When we use embeddings? Recommendations: Suggest similar songs, videos, movies, products Search: Get search results when keywords don't match Cluster: Grouping related things together A beginner might be confused in terms like: Vector, High Dimensional. This is an example of a vector: [0.9, 0.8, 0.1]. Array/List/Vector all mean the same thing. 'List' is just a plain english, 'array' is the programming term, vector is the math/ml term. High Dimensional: Multi-dimensional just means more than 1 - could be 2D, 3D, 10D,... too vague.…