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Hey guys, welcome to the channel. Today we will walk through a couple of papers by Mikolov and his team at Google that introduced Word2Vec back in 2013. Since then, Word2Vec has arguably become one of the most popular word embedding model and word embedding has become a de facto standard to replace feature engineering in almost natural language processing tasks. In this video, we will walk through some of the important sections of the paper. I will try supplementing the text with more explanation and illustrative examples which will hopefully help drive the understanding of the intuition behind some of the main ideas in the papers. So, a massive thank you to anyone who is watching this. Letamp;#39;s jump right into it! The first paper we are gonna look at is Efficient Estimations of Word Representations in Vector Space. Word Representations in Vector Space means exactly what it says, representing words as vectors. Letamp;#39;s take an example to get comfortable with the idea of using