Hands-On Natural Language Processing with PyTorch 1.x : Build smart, AI-driven linguistic applications using deep learning and NLP techniques
Language: English
Published by Packt Publishing, 2020
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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- Title
- Hands-On Natural Language Processing with PyTorch 1.x : Build smart, AI-driven linguistic applications using deep learning and NLP techniques
- Author
- Thomas Dop
- Publisher
- Packt Publishing
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1789802741
- ISBN 13
- 9781789802740
- Item weight
- 525 grams
- Dimensions
- 235x191x16 mm
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
- Fundamentals of Machine Learning and Deep Learning
- Getting Started with PyTorch 1.x for NLP
- NLP and Text Embeddings
- Text Preprocessing, Stemming, and Lemmatization
- Recurrent Neural Networks and Sentiment Analysis
- Convolutional Neural Networks for Text Classification
- Text Translation using Sequence to Sequence Neural Networks
- Building a Chatbot Using Attention-based Neural Networks
- The Road Ahead
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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