Isbn: 9781491978238 - natural language processing with pytorch: build intelligent language applications using deep learning (10 Ergebnisse)

Paperback. Zustand: Fair. 1. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

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Paperback. Zustand: Very Good.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Paperback. Zustand: Brand New. 250 pages. 9.25x7.00x0.50 inches. In Stock.

Zustand: New. In English.

Kartoniert / Broschiert. Zustand: New. 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.&Uumlber den AutorrnrnDelip Rao is the founder of Jo.

Zustand: New. 2019. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware - 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.…