Introduction to natural language processing with TensorFlow

Data Scientist
Visual Studio

In this module, we'll explore different neural network architectures for processing natural language texts. Natural Language Processing (NLP) has experienced fast growth and advancement primarily because the performance of the language models depends on their overall ability to "understand" text and can be trained using an unsupervised technique on large text corpora. Additionally, pre-trained text models (such as BERT) simplified many NLP tasks and has dramatically improved the performance. We'll learn more about these techniques and the basics of NLP in this learning module.

Learning objectives

In this module you will:

  • Understand how text is processed for natural language processing tasks
  • Get an introduced to Recurrent Neural Networks (RNNs) and Generative Neural Networks (GNNs)
  • Learn about Attention Mechanisms
  • Learn how to build text classification models


  • Basic Python knowledge
  • Basic understanding of machine learning