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Sanskar
Keen Learner and Exp... • 29d
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 lets discuss about what really is word embedding? Word embedding is a term used for the representation of words for text analysis typically in the form of a real valued vector that encodes the meaning of words in such a way that the words closer in vector space are expected to be similar in meaning. For example happy and excited are similar however angry is the opposite of happy. Word embeddings are of two types: 1. count or frequency: these are when words are represented in vectors based on how many times they appear in a document in corpus. 2. Deep learning trained model: these include word2vec which further include continuous bag of words and skipgram. And here are my notes.
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