You probably don't need to be convinced that NLP is such an exciting field. Conversational agents such as Amazon's Alexa and Google Assistant are already an essential part of our daily lives. Exciting new AI technologies like smart chatbots and text generation models are on popular media every day.

Thanks to popular frameworks such as TensorFlow and PyTorch, anyone can fire up a Python notebook and try the state-of-the-art deep learning models within minutes. But you may be wondering how to go about building your own models to solve NLP problems and completing the rest of your application. This book is here to answer these questions.

This is not a typical NLP or machine learning book. 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.

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