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.
