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Coles Implement NLP use-cases using BERT: Explore the Implementation of NLP Tasks Using the Deep Learning Framework and Python (English Edition) at Bayshore Shopping Centre in Ottawa, ON
Implement NLP use-cases using BERT: Explore the Implementation of NLP Tasks Using the Deep Learning Framework and Python (English Edition) in Ottawa, ON
Implement NLP use-cases using BERT: Explore the Implementation of NLP Tasks Using the Deep Learning Framework and Python (English Edition) in Ottawa, ON
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This book provides a solid foundation for ‘Natural Language Processing’ with pragmatic explanation and implementation of a wide variety of industry wide scenarios. After reading this book, one can simply jump to solve real world problems and join the league of NLP developers.It starts with the introduction of Natural Language Processing and provides a good explanation of different practical situations which are currently implemented across the globe. Thereafter, it takes a deep dive into the text classification with different types of algorithms to implement the same. Then, it further introduces the second important NLP use case called Named Entity Recognition with its popular algorithm choices. Thereafter, it provides an introduction to a state of the art language model called BERT and its application. After reading this book, you would be prepared to start picking any NLP applications, have a healthy discussion about the pros and cons of different approaches with other team members, and definitely implement a good NLP model.Finally, at the end of this book you will connect with all the theoretical discussions with code snippets (Python) which would be really helpful to implement into your domain-specific applications.
This book provides a solid foundation for ‘Natural Language Processing’ with pragmatic explanation and implementation of a wide variety of industry wide scenarios. After reading this book, one can simply jump to solve real world problems and join the league of NLP developers.It starts with the introduction of Natural Language Processing and provides a good explanation of different practical situations which are currently implemented across the globe. Thereafter, it takes a deep dive into the text classification with different types of algorithms to implement the same. Then, it further introduces the second important NLP use case called Named Entity Recognition with its popular algorithm choices. Thereafter, it provides an introduction to a state of the art language model called BERT and its application. After reading this book, you would be prepared to start picking any NLP applications, have a healthy discussion about the pros and cons of different approaches with other team members, and definitely implement a good NLP model.Finally, at the end of this book you will connect with all the theoretical discussions with code snippets (Python) which would be really helpful to implement into your domain-specific applications.
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