Deep Learning for Natural Language Processing (Hardcover)

Mr Ashish Suresh Patel

ISBN 13: 9798896730354

Editore: Notion Press, 2024

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Hardcover. Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP). The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers clear explanations of the theory behind the models, followed by practical code examples using popular deep learning frameworks like TensorFlow and PyTorch.Readers will learn how to preprocess text data, build word embeddings, and work with sequence models to handle real-world language data. The authors also cover cutting-edge techniques such as transformers, BERT, and GPT, which have revolutionized NLP tasks. Emphasizing both theory and hands-on practice, this book is ideal for students, researchers, and professionals looking to deepen their understanding of deep learning methods in the context of language.By the end of the book, readers will be equipped with the skills to tackle a wide range of NLP problems using deep learning, and will gain a deeper appreciation for the powerful combination of deep learning and natural language processing in transforming industries like healthcare, finance, and customer service. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9798896730354

Dati bibliografici

• Titolo: Deep Learning for Natural Language Processing (Hardcover)
• Editore: Notion Press
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 13: 9798896730354
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

Deep Learning for Natural Language Processing is a comprehensive guide that explores the intersection of deep learning techniques and natural language processing (NLP).

The book begins with an overview of fundamental NLP concepts and progresses into advanced deep learning architectures, including feedforward neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms. Each chapter offers clear explanations of the theory behind the models, followed by practical code examples using popular deep learning frameworks like TensorFlow and PyTorch.

Readers will learn how to preprocess text data, build word embeddings, and work with sequence models to handle real-world language data. The authors also cover cutting-edge techniques such as transformers, BERT, and GPT, which have revolutionized NLP tasks. Emphasizing both theory and hands-on practice, this book is ideal for students, researchers, and professionals looking to deepen their understanding of deep learning methods in the context of language.

By the end of the book, readers will be equipped with the skills to tackle a wide range of NLP problems using deep learning, and will gain a deeper appreciation for the powerful combination of deep learning and natural language processing in transforming industries like healthcare, finance, and customer service.