Apress | Hands-On Question Answering Systems Wth BERT: Applications In Neural Networks And Natural Language Processing (2021 EN)

Author: Navin Sabharwal, Amit Agrawal
Full Title: Hands-On Question Answering Systems Wth BERT: Applications In Neural Networks And Natural Language Processing
Publisher: Apress; 1st ed. edition (January 12, 2021)
Year: 2021
ISBN-13: 9781484266649 (978-1-4842-6664-9), 9781484266632 (978-1-4842-6663-2)
ISBN-10: 1484266641, 1484266633
Pages: 184
Language: English
Genre: Educational: Machine Learning
File type: EPUB (True), PDF (True), Code Files
Quality: 10/10
Price: 32.09 €


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.


Learn:
✓ 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

Features:
✓ 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

Who This Book Is For:
AI and machine learning developers and natural language processing developers.

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