Hands-on Question Answering Systems with BERT: Applications in Neural Networks a

Description: Hands-on Question Answering Systems with BERT by Navin Sabharwal, Amit Agrawal Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, youll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, youll cover word embedding and their types along with the basics of BERT. After this solid foundation, youll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. Youll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will LearnExamine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers. Back Cover Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, youll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, youll cover word embedding and their types along with the basics of BERT. After this solid foundation, youll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. Youll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. You will: Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Author Biography Navin is the chief architect for HCL DryICE Autonomics. He is an innovator, thought leader, author, and consultant in the areas of AI, machine learning, cloud computing, big data analytics, and software product development. He is responsible for IP development and service delivery in the areas of AI and machine learning, automation, AIOPS, public cloud GCP, AWS, and Microsoft Azure. Navin has authored 15+ books in the areas of cloud computing , cognitive virtual agents, IBM Watson, GCP, containers, and microservices. Amit Agrawal is a senior data scientist and researcher delivering solutions in the fields of AI and machine learning. He is responsible for designing end-to-end solutions and architecture for enterprise products. He has also authored and reviewed books in the area of cognitive virtual assistants. Table of Contents Chapter 1: Introduction to Natural Language Processing.- Chapter 2: Introduction to Word Embeddings.- Chapter 3: BERT Algorithms Explained.- Chapter 4: BERT Model Applications - Question Answering System.- Chapter 5: BERT Model Applications - Other tasks.- Chapter 6: Future of BERT models. Feature Integrates question answering systems with document repositories from different sources Contains an in-depth explanation of the technology behind BERT Takes a step-by-step approach to building question answering systems from scratch Details ISBN1484266633 Author Amit Agrawal Short Title Hands-On Question Answering Systems with BERT Language English Year 2021 ISBN-10 1484266633 ISBN-13 9781484266632 Format Paperback Subtitle Applications in Neural Networks and Natural Language Processing DOI 10.1007/978-1-4842-6664-9 Pages 184 Publication Date 2021-01-13 Publisher APress Edition 1st Imprint APress Place of Publication Berkley Country of Publication United States AU Release Date 2021-01-13 NZ Release Date 2021-01-13 US Release Date 2021-01-13 UK Release Date 2021-01-13 Illustrations 80 Illustrations, black and white; XV, 184 p. 80 illus. Edition Description 1st ed. 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