The advent of deep learning has been transformative for many difficult problems in machine learning, often delivering breakthrough performance compared with previous techniques. This paradigm shift has swept over the field of natural language processing, where an emerging deep learning approach has set the state-of-the-art in text categorization, information extraction, recommendations, and more.

Deep Learning with Text is a practitioner's guide that will help you learn how the neural networks that power modern natural language processing techniques work "under the hood." You'll find examples using "batteries-included" libraries in Python--including spaCy, gensim, and others--for applying this modern, deep learning approach to solve real-world problems with natural language text.

Until now, much of the published material about deep learning has been sequestered in research papers and graduate-level academic textbooks. This practical book will enable software developers and data scientists to build new products and systems that have only become possible in the past couple of years.