Hands-On Natural Language Processing with PyTorch 1.x
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Hands-On Natural Language Processing with PyTorch 1.x
By:
Overview of this book
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.
Table of Contents (14 chapters)
Preface
Section 1: Essentials of PyTorch 1.x for NLP
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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
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