Deep Learning for Natural Language Processing (Paperback)

Mr Ashish Suresh Patel

ISBN 13: 9798896730347

Verlag: Notion Press, 2024

Beschreibung

Paperback. 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 multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798896730347

Bibliografische Details

Titel: Deep Learning for Natural Language ...

Verlag: Notion Press

Erscheinungsdatum: 2024

Einband: Paperback

Zustand: new

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Inhaltsangabe:

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.