Book Image

Advanced Natural Language Processing with TensorFlow 2

By : Ashish Bansal, Tony Mullen
Book Image

Advanced Natural Language Processing with TensorFlow 2

By: Ashish Bansal, Tony Mullen

Overview of this book

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
Table of Contents (13 chapters)
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Installation and Setup Instructions for Code

Instructions for setting up an environment for the code in the book are provided in this chapter. These instructions:

  • Have been tested on macOS 10.15 and Ubuntu 18.04.3 LTS. You may have to translate these instructions for Windows.
  • Only cover the CPU version of TensorFlow. For the latest GPU installation instructions, please follow Please note that the use of a GPU is highly recommended. It will cut down the training times of complex models from days to hours.

The installation uses Anaconda and pip. It is assumed that Anaconda is set up and ready to go on your machine. Note that we use some new and some uncommon packages. These packages may not be available through conda. We will use pip in such cases.


  • On macOS: conda 49.2, pip 20.3.1
  • On Ubuntu: conda 4.6.11, pip 20.0.2