Word embedding is one of the most important concepts in Natural Language Processing (NLP). It is an NLP technique where words or phrases (i.e., strings) from a vocabulary are mapped to vectors of real numbers. The need to map strings into vectors of real numbers originated from computers' inability to do operations with strings. Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Before diving into word embedding, let's compare these three options to see why Word embedding is the best.

This is a follow-up tutorial prepared after Part I of the tutorial, Mastering Word Embeddings in 10 Minutes with TensorFlow, where we introduce several word vectorization concepts such as One Hot Encoding and Encoding with a Unique ID Value. I would highly recommend you to check this tutorial if you are new to natural language processing. In Part II of the tutorial, we will vectorize our words and trained their values using the IMDB Reviews dataset. This tutorial is our own take on TensorFlow's tutorial on word embedding. We will train a word embedding using a simple Keras model and the IMDB Reviews dataset.