Mastering Word Embeddings in 10 Minutes with TensorFlow

Mastering Word Embeddings in 10 Minutes with TensorFlow

Covering the Basics of Word Embedding, One Hot Encoding, Text Vectorization, Embedding Layers, and an Example Neural Network Architecture…

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.

There are several NLP techniques to convert strings into representative numbers, such as:

  • One Hot Encoding
  • Encoding with a Unique Number
  • Word Embedding

Before diving into word embedding, let’s compare these three options to see why Word embedding is the best.

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