Items related to Natural Language Processing Fundamentals for Developers
Synopsis
This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included.
FEATURES:
- Covers extensive topics related to natural language processing
- Includes separate appendices on regular expressions and probability/statistics
- Features companion files with source code and figures from the book.
The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com.
"synopsis" may belong to another edition of this title.
About the Author
Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty-five books including Google Gemini for Python, Large Language Models, and GPT-4 for Developers (all Mercury Learning).
"About this title" may belong to another edition of this title.
- PublisherMercury Learning and Information
- Publication date2021
- ISBN 10 1683926579
- ISBN 13 9781683926573
- BindingPaperback
- LanguageEnglish
- Number of pages364
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