Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps.
Unlike many online tutorials and textbooks, you won't learn how to write the backpropagation algorithm or activation layers. In fact, there are no mathematical formulae in this book. Not a single one. Instead, you'll quickly learn how to build practical NLP applications such as sentiment analyzers and spelling correctors and how to deploy them in production. You'll also learn the basic building blocks of neural networks and how to combine them to solve your own NLP problems. Thanks to powerful NLP frameworks such as AllenNLP and fairseq, this has never been easier.!
As a reader of this book, you'll want to be already familiar with the Python programming language and its ecosystem. But you don't need to have any prior knowledge of machine learning or natural language processing. In this book, I take a top-down approach, where we first focus on building a working NLP application quickly, and then related concepts are introduced as needed.
The book covers important topics for modern NLP, including RNNs and Seq2Seq models.
You can learn to build practical NLP applications within the first few chapters.
You don't need to know fancy math to read the book.
The book also covers state-of-the-art NLP models including ELMo and BERT.