Natural Language Processing with PyTorchlow
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€84.99
A01=Brian McMahan
A01=Delip Rao
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AI artificial intelligence PyTorch deep learning natural language processing NLP natural language understanding NLU text mining sequence modeling LTSM neural nets neural networks convolutional networks text classification
Author_Brian McMahan
Author_Delip Rao
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Product details
- ISBN 9781491978238
- Weight: 506g
- Dimensions: 180 x 238mm
- Publication Date: 05 Feb 2019
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.
Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.
Explore computational graphs and the supervised learning paradigm
Master the basics of the PyTorch optimized tensor manipulation library
Get an overview of traditional NLP concepts and methods
Learn the basic ideas involved in building neural networks
Use embeddings to represent words, sentences, documents, and other features
Explore sequence prediction and generate sequence-to-sequence models
Learn design patterns for building production NLP systems
Delip Rao is a machine learning and natural language processing researcher focused on building AI solutions for consumers and businesses. He has worked on NLP and ML research problems involving semi-supervised learning, graph-based ranking, sequence learning, distributed machine learning, and more, and has published several highly cited papers in these areas. Brian McMahan is a research engineer at Joostware, a San Francisco-based company specializing in consulting and building intellectual property in natural language processing and deep learning. He has a PhD in Computer Science from Rutgers University where he built Bayesian and Deep Learning models of language and semantics as they apply to machine perception in interactive situations.
