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Hands-on question answering systems ...

Sabharwal, Navin.

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Hands-on question answering systems with BERT = applications in neural networks and natural language processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hands-on question answering systems with BERT/ by Navin Sabharwal, Amit Agrawal.
Reminder of title:	applications in neural networks and natural language processing /
Author:	Sabharwal, Navin.
other author:	Agrawal, Amit.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xv, 184 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-1-4842-6664-9
ISBN:	9781484266649

Hands-on question answering systems with BERT = applications in neural networks and natural language processing /
Sabharwal, Navin.

Hands-on question answering systems with BERTapplications in neural networks and natural language processing /[electronic resource] :by Navin Sabharwal, Amit Agrawal. - Berkeley, CA :Apress :2021. - xv, 184 p. :ill., digital ;24 cm.

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.

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, you'll 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, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll 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.

ISBN: 9781484266649

Standard No.: 10.1007/978-1-4842-6664-9doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .S234 2021

Dewey Class. No.: 006.32

Hands-on question answering systems with BERT = applications in neural networks and natural language processing /
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505 0 $a 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.
520 $a 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, you'll 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, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll 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.
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