Word2Vec: Build Semantic Recommender System with TensorFlow

In this Word2Vec tutorial, you will learn how to train a Word2Vec Python model and use it to semantically suggest names based on one or even two given names. This Word2Vec tutorial is meant to highlight the interesting, substantive parts of building a word2vec Python model with TensorFlow. Word2vec is a group of related models that are used to produce Word Embeddings.

Embedding vectors created using the Word2vec algorithm have many advantages compared to earlier algorithms such as latent semantic analysis. Word embedding is one of the most popular representation of document vocabulary. It is capable of capturing context of a word in a document, semantic and syntactic similarity, relation with other words, etc.

Word Embeddings are vector representations of a particular word. The best way to understand an algorithm is to implement it. So, in this course you will learn Word Embeddings by implementing it in the Python library, TensorFlow.

Word2Vec is one of the most popular techniques to learn word embeddings using shallow neural network. Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. In this Word2Vec tutorial, you will learn The idea behind Word2Vec:Take a 3 layer neural network.