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Hands-On Natural Language Processing with PyTorch 1.x

You're reading from   Hands-On Natural Language Processing with PyTorch 1.x Build smart, AI-driven linguistic applications using deep learning and NLP techniques

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Essentials of PyTorch 1.x for NLP
2. Chapter 1: Fundamentals of Machine Learning and Deep Learning FREE CHAPTER 3. Chapter 2: Getting Started with PyTorch 1.x for NLP 4. Section 2: Fundamentals of Natural Language Processing
5. Chapter 3: NLP and Text Embeddings 6. Chapter 4: Text Preprocessing, Stemming, and Lemmatization 7. Section 3: Real-World NLP Applications Using PyTorch 1.x
8. Chapter 5: Recurrent Neural Networks and Sentiment Analysis 9. Chapter 6: Convolutional Neural Networks for Text Classification 10. Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks 11. Chapter 8: Building a Chatbot Using Attention-Based Neural Networks 12. Chapter 9: The Road Ahead 13. Other Books You May Enjoy

Chapter 2: Getting Started with PyTorch 1.x for NLP

PyTorch is a Python-based machine learning library. It consists of two main features: its ability to efficiently perform tensor operations with hardware acceleration (using GPUs) and its ability to build deep neural networks. PyTorch also uses dynamic computational graphs instead of static ones, which sets it apart from similar libraries such as TensorFlow. By demonstrating how language can be represented using tensors and how neural networks can be used to learn from NLP, we will show that both these features are particularly useful for natural language processing.

In this chapter, we will show you how to get PyTorch up and running on your computer, as well as demonstrate some of its key functionalities. We will then compare PyTorch to some other deep learning frameworks, before exploring some of the NLP functionality of PyTorch, such as its ability to perform tensor operations, and finally demonstrate how to build a simple neural...

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