Keen Learner and Exp...ย โขย 11d
Day 14 of learning AI/ML as a beginner. Topic: Word2vec I think I am getting lost and that I have omitted some core concepts as there are many things I believe I am unfamiliar with and I am searching for some guidance. Can anybody please tell me what all things I should learn and in which order I should learn them? because I think I have erroneously jumped to an advance topic before learning some fundamentals. Anyways here's what I understood about word2vec. Word2vec is a natural language processing technique by google. It uses neural network model to learn word association from a large corpus of text. Word2vec represents each distinct word with a particular list of numbers called a vector. It is based on feature representation i.e. it divides words into various categories and then correlate words with those categories to find their correlation. Then we used cosine similarity and distance formula to find the difference between two words and if they are related to each other or not. Similar words are closely related and different words are not. I could have understood this more better if I had not erroneously omitted some important fundament topics please do tell me which all things should I learn and in which order so that I can get going in the right direction. And here are my notes of word2vec.
Keen Learner and Exp...ย โขย 12d
Day 13 of learning AI/ML as a beginner. Topic: Word Embedding. I have discussed about one hot encoding, Bag of words and TF-IDF in my recent posts. These are the count or frequency tools that are a part of word embedding but before moving forward l
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