Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

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 a nd 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. 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.