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Hands-On Natural Language Processing with PyTorch 1.x
Hands-On Natural Language Processing with PyTorch 1.x

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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Hands-On Natural Language Processing with PyTorch 1.x

Chapter 1: Fundamentals of Machine Learning and Deep Learning

Our world is rich with natural language data. Over the past several decades, the way we communicate with one another has shifted to the digital realm and, as such, this data can be used to build models that can improve our online experience. From returning relevant results within a search engine, to autocompleting the next word you type in an email, the benefits of being able to extract insights from natural language is clear to see.

While the way we, as humans, understand language differs notably from the way a model or artificial intelligence may understand it, by shedding light on machine learning and what it is used for, we can begin to understand just how these deep learning models understand language and what fundamentally happens when a model learns from data.

Throughout this book, we will explore this application of artificial intelligence and deep learning to natural language. Through the use of PyTorch,...

Overview of machine learning

Fundamentally, machine learning is the algorithmic process used to identify patterns and extract trends from data. By training specific machine learning algorithms on data, a machine learning model may learn insights that aren't immediately obvious to the human eye. A medical imaging model may learn to detect cancer from images of the human body, while a sentiment analysis model may learn that a book review containing the words good, excellent, and entertaining is more likely to be a positive review than one containing the words bad, terrible, and boring.

Broadly speaking, machine learning algorithms fall into two main categories: supervised learning and unsupervised learning.

Supervised learning

Supervised learning covers any task where we wish to use an input to predict an output. Let's say we wish to train a model to predict house prices. We know that larger houses tend to sell for more money, but we don't know the exact...

Neural networks

In our previous examples, we have discussed mainly regressions in the form . We have touched on using polynomials to fit more complex equations such as . However, as we add more features to our model, when to use a transformation of the original feature becomes a case of trial and error. Using neural networks, we are able to fit a much more complex function, y = f(X), to our data, without the need to engineer or transform our existing features. 

Structure of neural networks

When we were learning the optimal value of , which minimized loss in our regressions, this is effectively the same as a one-layer neural network:

Figure 1.10 – One-layer neural network

Here, we take each of our features, , as an input, illustrated here by a node. We wish to learn the parameters, , which are represented as connections in this diagram. Our final sum of all the products between and gives us our final prediction, y:

A neural network...

NLP for machine learning

Unlike humans, computers do not understand text – at least not in the same way that we do. In order to create machine learning models that are able to learn from data, we must first learn to represent natural language in a way that computers are able to process.

When we discussed machine learning fundamentals, you may have noticed that loss functions all deal with numerical data so as to be able to minimize loss. Because of this, we wish to represent our text in a numerical format that can form the basis of our input into a neural network. Here, we will cover a couple of basic ways of numerically representing our data. 

Bag-of-words

The first and most simple way of representing text is by using a bag-of-words representation. This method simply counts the words in a given sentence or document and counts all the words. These counts are then transformed into a vector where each element of the vector is the count of the times each word in the...

Summary

In this chapter, we introduced the fundamentals of machine learning and neural networks, as well as a brief overview of transforming text for use within these models. In the next chapter, we will provide a brief overview of PyTorch and how it can be used to construct some of these models.

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Key benefits

  • Get to grips with word embeddings, semantics, labeling, and high-level word representations using practical examples
  • Learn modern approaches to NLP and explore state-of-the-art NLP models using PyTorch
  • Improve your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNs

Description

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.

Who is this book for?

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.

What you will learn

  • 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

Product Details

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Publication date : Jul 09, 2020
Length: 276 pages
Edition : 1st
Language : English
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Publication date : Jul 09, 2020
Length: 276 pages
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Table of Contents

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