Hands-On Natural Language Processing with PyTorch 1.x

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

Thomas Dop 지음

Packt(GCO Science) 2021년 07월 09일 출간

작품소개

책소개

Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data

▶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

▶Key Features
？ 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

▶Who This Book Is 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.

목차

▶TABLE of CONTENTS
▷ 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

책 속으로

▶What this book covers
？ Chapter 1, Fundamentals of Machine Learning and Deep Learning, provides an overview of the fundamental aspects of machine learning and neural networks.

？ Chapter 2, Getting Started with PyTorch 1.x for NLP, shows you how to download, install, and start PyTorch. We will also run through some of the basic functionality of the package.

？ Chapter 3, NLP and Text Embeddings, shows you how to create text embeddings for NLP and use them in basic language models.

？ Chapter 4, Text Preprocessing, Stemming, and Lemmatization, shows you how to preprocess textual data for use in NLP deep learning models.

？ Chapter 5, Recurrent Neural Networks and Sentiment Analysis, runs through the fundamentals of recurrent neural networks and shows you how to use them to build a sentiment analysis model from scratch.

？ Chapter 6, Convolutional Neural Networks for Text Classification, runs through the fundamentals of convolutional neural networks and shows you how you can use them to build a working model for classifying text.

？ Chapter 7, Text Translation Using Sequence-to-Sequence Neural Networks, introduces the concept of sequence-to-sequence models for deep learning and runs through how to use them to construct a model to translate text into another language.

？ Chapter 8, Building a Chatbot Using Attention-Based Neural Networks, covers the concept of attention for use within sequence-to-sequence deep learning models and also shows you how they can be used to build a fully working chatbot from scratch.

？ Chapter 9, The Road Ahead, covers some of the state-of-the-art models currently used within NLP deep learning and looks at some of the challenges and problems facing the field of NLP going forward.

출판사 서평

▶ Preface
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.

작가정보

저자(글) 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.