Hands-on Question Answering Systems with BERT

Applications in Neural Networks and Natural Language Processing de

,

Éditeur :

Apress


Paru le : 2021-01-12

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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, 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.
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.
Pages
184 pages
Collection
n.c
Parution
2021-01-12
Marque
Apress
EAN papier
9781484266632
EAN PDF
9781484266649

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
18
Taille du fichier
4987 Ko
Prix
46,34 €
EAN EPUB
9781484266649

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
18
Taille du fichier
6451 Ko
Prix
46,34 €

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