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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

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781789802740

Pages 276 pages

Edition 1st Edition

Languages

Concepts

Mobile Application Development

Author (1):

Thomas Dop

Table of Contents (14) Chapters

Preface

Section 1: Essentials of PyTorch 1.x for NLP

Chapter 1: Fundamentals of Machine Learning and Deep Learning

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

Section 2: Fundamentals of Natural Language Processing

Chapter 3: NLP and Text Embeddings

Chapter 4: Text Preprocessing, Stemming, and Lemmatization

Section 3: Real-World NLP Applications Using PyTorch 1.x

Chapter 5: Recurrent Neural Networks and Sentiment Analysis

Chapter 6: Convolutional Neural Networks for Text Classification

Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks

Chapter 8: Building a Chatbot Using Attention-Based Neural Networks

Chapter 9: The Road Ahead

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Section 3: Real-World NLP Applications Using PyTorch 1.x

In this section, we will use the various Natural Language Processing (NLP) techniques available in PyTorch to build a wide range of real-world applications using PyTorch. Sentiment analysis, text summarization, text classification, and building a chatbot application using PyTorch are some of the tasks that will be covered in this section.

This section contains the following chapters:

Chapter 5, Recurrent Neural Networks and Sentiment Analysis
Chapter 6, Convolutional Neural Networks for Text Classification
Chapter 7, Text Translation Using Sequence-to-Sequence Neural Networks
Chapter 8, Building a Chatbot Using Attention-Based Neural Networks
Chapter 9, The Road Ahead

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Hands-On Natural Language Processing with PyTorch 1.x

Published in: Jul 2020 Publisher: Packt ISBN-13: 9781789802740

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Authors (1)

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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