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.
publication date
2020-07-09
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
approach
Complete with step-by-step explanations of essential concepts, practical examples you will begin familiarizing yourself with NLP techniques and the latest deep learning methodologies before learning how to apply these theories in practice and build these models for yourself.
audience
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.
short description
Developers working with NLP will be able to put their knowledge to work with this practical guide to PyTorch. You will learn to use PyTorch offerings and how to understand and analyze text using Python. You will learn to extract the underlying meaning in the text using deep neural networks and modern deep learning algorithms.
subtitle
Build smart, AI-driven linguistic applications using deep learning and NLP techniques
keywords
convolutional neural network, recurrent neural network, LSTM neural network, LSTM deep learning, LSTM network, Text analysis, deep learning, machine learning, deep neural networks
Product ISBN
9781789802740
