Get hands-on knowledge of how(Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing ( ) and deep learning.
The book begins with an overview of the technology landscape behind. It takes you through the basics of , 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 .
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.
What You Will 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
Who This Book Is For
AI and machine learning developers and natural language processing developers.
标签：BERT, NLP, 机器学习
Author: Amit Agrawal, Navin Sabharwal
Length: 199 pages
Publication Date: 2021-01-27