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Hands-On Natural Language Processing with PyTorch 1.x

Hands-On Natural Language Processing with PyTorch 1.x

This is the code repository for Hands-On Natural Language Processing with PyTorch 1.x, published by Packt.

Build smart, AI-driven linguistic applications using deep learning and NLP techniques

What is this book about?

Developers working with NLP will be able to put their knowledge to work with this practical guide to PyTorch. You will learn to use PyTorch offerings and how to understand and analyze text using Python. You will learn to extract the underlying meaning in the text using deep neural networks and modern deep learning algorithms.

This book covers the following exciting features:

  • Use NLP techniques for understanding, processing, and generating text
  • Understand PyTorch, its applications and how it can be used to build deep linguistic models
  • Explore the wide variety of deep learning architectures for NLP
  • Develop the skills you need to process and represent both structured and unstructured NLP data
  • Become well-versed with state-of-the-art technologies and exciting new developments in the NLP domain
  • Create chatbots using attention-based neural networks

If you feel this book is for you, get your copy today!

https://www.packtpub.com/

Instructions and Navigations

All of the code is organized into folders.

The code will look like the following:

word_dict = {}
inverse_word_dict = {}

for i, word in enumerate(corpus):
   word_dict[word] = i
   inverse_word_dict[i] = word

Following is what you need for this book: This PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you’re looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.

With the following software and hardware list you can run all code files present in the book (Chapter 2 - 8).

Errata

Software and Hardware List

Chapter Software required OS required
2 - 8 Python 3.7, PyTorch 1.x, GPU (preferred) Windows/Linux/macOS

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

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Get to Know the Author

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.

Suggestions and Feedback

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