Hands-On Natural Language Processing with PyTorch 1.x

By Thomas Dop
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    Section 1: Essentials of PyTorch 1.x for NLP
About this book

In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks.

Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots.

By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.

Publication date:
July 2020
Publisher
Packt
Pages
276
ISBN
9781789802740

 

Section 1: Essentials of PyTorch 1.x for NLP

In this section, you will learn about the basic concepts of PyTorch 1.x in the context of Natural Language Processing (NLP). You will also learn how to install PyTorch 1.x on your machine, as well as how to use CUDA to accelerate the processing speed.

This section contains the following chapters:

  • Chapter 1, Fundamentals of Machine Learning and Deep Learning
  • Chapter 2, Getting Started with PyTorch 1.x for NLP
About the Author
  • Thomas Dop

    Thomas Dop is a data scientist at MagicLab, a company that creates leading dating apps, including Bumble and Badoo. He works on a variety of areas within data science, including NLP, deep learning, computer vision, and predictive modeling. He holds an MSc in data science from the University of Amsterdam.

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Hands-On Natural Language Processing with PyTorch 1.x
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