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  • Continental Academy Press

    Published by Continental Academy Press, London

    Seller: Continental Academy Press, London, SELEC, United Kingdom

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    Softcover. Condition: New. Dust Jacket Condition: no dj. First. Using Recurrent Neural Networks for Natural Language Processing provides a thorough introduction to the principles and applications of recurrent neural networks (RNNs) in natural language processing tasks. By exploring the architecture, training, and optimization of RNNs, this book helps readers understand how to design and implement effective natural language processing systems that can accurately classify and recognize text and speech. With its focus on theoretical foundations and practical applications, Using Recurrent Neural Networks for Natural Language Processing is an essential resource for anyone working in natural language processing, machine learning, or artificial intelligence. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

  • Continental Academy Press

    Published by Continental Academy Press, London

    Seller: Continental Academy Press, London, SELEC, United Kingdom

    Seller rating 5 out of 5 stars 5-star rating, Learn more about seller ratings

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    £ 7.50 shipping from United Kingdom to U.S.A.

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    Softcover. Condition: New. Dust Jacket Condition: no dj. First. Natural Language Generation (NLG) is a critical component of many industries, from chatbots to content creation, yet the complexity of these data sets can be overwhelming. 'Using Recurrent Neural Networks for Natural Language Generation' offers a cutting-edge exploration of recurrent neural networks (RNNs), a powerful technique for modeling and generating natural language. By examining the applications and limitations of RNNs in NLG, this book provides a comprehensive overview of the latest developments in the field, including the use of long short-term memory (LSTM) networks and gated recurrent units (GRUs). With its focus on practical implementation and real-world examples, readers will gain a deeper understanding of how to harness the power of RNNs to build more accurate, efficient, and effective NLG systems. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.