Hands-On Natural Language Processing with PyTorch 1.x

<Hands-On Natural Language Processing with PyTorch 1.x> 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.

▶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.