Near-lifelike chatbots, meaningful resume-to-job matches, and laser-focused product recommendations are just a few examples of what’s possible when you apply deep learning to natural language processing (NLP). Emerging NLP algorithms and machine learning techniques give these amazing systems the ability to determine emotional tone, infer meaning from context, summarize documents, and even generate helpful responses to new questions.
Exploring Deep Learning for Language is a collection of chapters from five Manning books, handpicked by machine learning expert Jeff Smith. This free eBook begins with an overview of natural language processing before moving on to techniques for working with language data. You’ll explore practical techniques like feature generation to help algorithms make sense of your unstructured data and generating synonyms for improving relevant query results. You’ll also get an overview of more advanced topics like using artificial neural networks to model language and embedding natural language in the popular TensorFlow machine learning framework. These carefully-selected chapters deliver a solid foundation for what you can do when you combine deep learning with natural language processing.
"Packets of thought (NLP overview)" from Natural Language Processing in Action by Hobson Lane, Cole Howard, and Hannes Hapke
"Generating features" from Machine Learning Systems by Jeff Smith
"Generating synonyms" from from Deep Learning for Search by Tommaso Teofili
"Neural Networks that understand language" from Grokking Deep Learning by Andrew Trask
"Sequence-to-sequence models for chatbots" from Machine Learning with TensorFlow by Nishant Shukla
Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https://medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. He’s the author of Machine Learning Systems from Manning.
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