Sudharsan Ravichandiran - Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT
Here you can read online Sudharsan Ravichandiran - Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, publisher: Packt Publishing Ltd, genre: Children. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT
- Author:
- Publisher:Packt Publishing Ltd
- Genre:
- Year:2021
- Rating:4 / 5
- Favourites:Add to favourites
- Your mark:
Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Faces transformers library
Key Features- Explore the encoder and decoder of the transformer model
- Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT
- Discover how to pre-train and fine-tune BERT models for several NLP tasks
BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Googles BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformers encoder and decoder work.
Youll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, youll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. Youll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, youll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT.
By the end of this BERT book, youll be well-versed with using BERT and its variants for performing practical NLP tasks.
What you will learn- Understand the transformer model from the ground up
- Find out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasks
- Get hands-on with BERT by learning to generate contextual word and sentence embeddings
- Fine-tune BERT for downstream tasks
- Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models
- Get the hang of the BERT models based on knowledge distillation
- Understand cross-lingual models such as XLM and XLM-R
- Explore Sentence-BERT, VideoBERT, and BART
This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.
Sudharsan Ravichandiran: author's other books
Who wrote Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT? Find out the surname, the name of the author of the book and a list of all author's works by series.
Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Kunal Parikh
Publishing Product Manager: Devika Battike
Content Development Editor: Sean Lobo
Senior Editor: Roshan Kumar
Technical Editor: Manikandan Kurup
Copy Editor: Safis Editing
Project Coordinator: Aishwarya Mohan
Proofreader: Safis Editing
Indexer: Priyanka Dhadke
Production Designer: Prashant Ghare
First published: January 2021
Production reference: 1210121
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-83882-159-3
www.packt.com
To my adorable mom, Kasthuri, and to my beloved dad, Ravichandiran.
Packt.com
Subscribe to our online digital library for full access to over 7,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.
Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals
Improve your learning with Skill Plans built especially for you
Get a free eBook or video every month
Fully searchable for easy access to vital information
Copy and paste, print, and bookmark content
Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at customercare@packtpub.com for more details.
At www.packt.com , you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.
Sudharsan Ravichandiran is a data scientist, researcher, and bestselling author. He completed his bachelor's in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, including natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.
Dr. ArmandoFandango creates AI-empowered products by leveraging reinforcement learning, deep learning, and distributed computing. Armando has provided thought leadership in diverse roles at small and large enterprises, including Accenture, Nike, Sonobi, and IBM, along with advising high-tech AI-based start-ups. Armando has authored several books, including Mastering TensorFlow, TensorFlow Machine Learning Projects, and Python Data Analysis, and has published research in international journals and presented his research at conferences. Dr. Armandos current research and product development interests lie in the areas of reinforcement learning, deep learning, edge AI, and AI in simulated and real environments (VR/XR/AR).
Ashwin Sreenivas is the cofounder and chief technology officer of Helia AI, a computer vision company that structures and understands the world's video. Prior to this, he was a deployment strategist at Palantir Technologies. Ashwin graduated in Phi Beta Kappa from Stanford University with a master's degree in artificial intelligence and a bachelor's degree in computer science.
Gabriel Bianconi is the founder of Scalar Research, an artificial intelligence and data science consulting firm. Past clients include start-ups backed by YCombinator and leading venture capital firms (for example, Scale AI, and Fandom), investment firms, and their portfolio companies (for example, the Two Sigma-backed insurance firm MGA), and large enterprises (for example, an industrial conglomerate in Asia, and a leading strategy consulting firm). Beyond consulting, Gabriel is a frequent speaker at major technology conferences and a reviewer on top academic conferences (for example, ICML) and AI textbooks. Previously, he received B.S. and M.S. degrees in computer science from Stanford University, where he conducted award-winning research in computer vision and deep learning.
Mani Kanteswara has a bachelor's and a master's in finance (tech) from BITS Pilani with over 10 years of strong technical expertise and statistical knowledge of analytics. He is currently working as a lead strategist with Google and has previously worked as a senior data scientist at WalmartLabs. He has worked in deep learning, computer vision, machine learning, and the natural language processing space building solutions/frameworks capable of solving different business problems and building algorithmic products. He has extensive expertise in solving problems in IoT, telematics, social media, the web, and the e-commerce space. He strongly believes that learning concepts with a practical implementation of the subject and exploring its application areas leads to a great foundation.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Next pageFont size:
Interval:
Bookmark:
Similar books «Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT»
Look at similar books to Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.