Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By : Thomas Dop
Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By: Thomas Dop

Overview of 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.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Essentials of PyTorch 1.x for NLP
Section 1: Essentials of PyTorch 1.x for NLP
Free Chapter
2
Chapter 1: Fundamentals of Machine Learning and Deep Learning
Chapter 1: Fundamentals of Machine Learning and Deep Learning
Overview of machine learning
Neural networks
NLP for machine learning
Summary
3
Chapter 2: Getting Started with PyTorch 1.x for NLP
Chapter 2: Getting Started with PyTorch 1.x for NLP
Technical requirements
Installing and using PyTorch 1.x
Enabling PyTorch acceleration using CUDA
Comparing PyTorch to other deep learning frameworks
Building a simple neural network in PyTorch
NLP for PyTorch
Summary
4
Section 2: Fundamentals of Natural Language Processing
In this section, you will learn about the fundamentals of building a Natural Language Processing (NLP) application. You will also learn how to use various NLP techniques, such as word embeddings, CBOW, and tokenization in PyTorch in this section.
5
Chapter 3: NLP and Text Embeddings
Chapter 3: NLP and Text Embeddings
Technical requirements
Embeddings for NLP
Exploring CBOW
Exploring n-grams
Tokenization
Tagging and chunking for parts of speech
TF-IDF
Summary
6
Chapter 4: Text Preprocessing, Stemming, and Lemmatization
Chapter 4: Text Preprocessing, Stemming, and Lemmatization
Technical requirements
Text preprocessing
Stemming and lemmatization
Uses of stemming and lemmatization
Summary
7
Section 3: Real-World NLP Applications Using PyTorch 1.x
Section 3: Real-World NLP Applications Using PyTorch 1.x
8
Chapter 5: Recurrent Neural Networks and Sentiment Analysis
Chapter 5: Recurrent Neural Networks and Sentiment Analysis
Technical requirements
Building RNNs
Introducing LSTMs
Building a sentiment analyzer using LSTMs
Deploying the application on Heroku
Summary
9
Chapter 6: Convolutional Neural Networks for Text Classification
Chapter 6: Convolutional Neural Networks for Text Classification
Technical requirements
Exploring CNNs
Building a CNN for text classification
Summary
10
Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks
Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks
Technical requirements
Theory of sequence-to-sequence models
Building a sequence-to-sequence model for text translation
Next steps
Summary
11
Chapter 8: Building a Chatbot Using Attention-Based Neural Networks
Chapter 8: Building a Chatbot Using Attention-Based Neural Networks
Technical requirements
The theory of attention within neural networks
Building a chatbot using sequence-to-sequence neural networks with attention
Summary
12
Chapter 9: The Road Ahead
Chapter 9: The Road Ahead
Exploring state-of-the-art NLP machine learning
Future NLP tasks
Summary
13
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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

import torch

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

word_1 = ‘cat'
word_2 = ‘dog'
word_3 = ‘bird'

Any command-line input or output is written as follows:

$ mkdir flaskAPI
$ cd flaskAPI

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

Appear like this.

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