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

Thomas Dop

ISBN 10: 1789802741 / ISBN 13: 9781789802740

Verlag: Packt Publishing, 2020

Beschreibung

Bestandsnummer des Verkäufers I-9781789802740

Bibliografische Details

• Titel: Hands-On Natural Language Processing with PyTorch 1.x: Build smart, AI-driven linguistic applications using deep learning and NLP techniques
• Verlag: Packt Publishing
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 1789802741
• ISBN-13: 9781789802740
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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

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

Book 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.

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

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.

Table of Contents

1. Fundamentals of Machine Learning and Deep Learning
2. Getting Started with PyTorch 1.x for NLP
3. NLP and Text Embeddings
4. Text Preprocessing, Stemming, and Lemmatization
5. Recurrent Neural Networks and Sentiment Analysis
6. Convolutional Neural Networks for Text Classification
7. Text Translation using Sequence to Sequence Neural Networks
8. Building a Chatbot Using Attention-based Neural Networks
9. The Road Ahead

Über die Autorin bzw. den Autor

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.