Denis Rothman - Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
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- Book:Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
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Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models
Key Features- Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models
- Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
- Learn training tips and alternative language understanding methods to illustrate important key concepts
The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.
The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.
By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
What you will learn- Use the latest pretrained transformer models
- Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
- Create language understanding Python programs using concepts that outperform classical deep learning models
- Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
- Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
- Measure the productivity of key transformers to define their scope, potential, and limits in production
Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.
Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
Denis Rothman: author's other books
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Transformers for Natural Language Processing
Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
Denis Rothman
BIRMINGHAM - MUMBAI
Transformers for Natural Language Processing
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.
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First published: January 2021
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Published by Packt Publishing Ltd.
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ISBN 978-1-80056-579-1
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Denis Rothman graduated from Sorbonne University and Paris Diderot University, designing one of the very first word2matrix patented embedding and vectorizing systems. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Mot et Chandon and other companies. He has authored an AI resource optimizer for IBM and apparel producers and an advanced planning and scheduling (APS) solution used worldwide.
I want to thank the corporations who trusted me from the start to deliver artificial intelligence solutions and share the risks of continuous innovation. I also thank my family, who believed I would make it big at all times.
George Mihaila is currently a PhD candidate in computer science at University of North Texas. The main research areas he works on are Deep Learning and Natural Language Processing (NLP) with a focus on dialogue generation. His research thesis is on casual dialogue generation with persona.
George is very passionate when it comes to AI and NLP. He always keeps up with the latest language models. Every time a new groundbreaking model comes along, he likes to study the code to better understand its inner workings.
Besides his research, George is also involved in writing tutorials on how to use transformer models in various machine learning tasks. He loves the idea of open source and likes sharing his knowledge and helping others in NLP through his GitHub projects and personal website.
In his free time, George likes to cook and travel with his significant other.
Thanks to everyone on the publishing team and to Denis Rothman for allowing me this opportunity and for making the review process so much fun and easy.
Malte Pietsch is co-founder and CTO at deepset, where he builds Haystack an end-to-end framework for building enterprise search engines fueled by open source and NLP. He holds an M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset, he worked as a data scientist for multiple startups. He is an open-source lover, likes reading papers before breakfast, and is obsessed with automating the boring parts of his work.
Carlos Toxtli is a human-computer interaction researcher who studies the impact of artificial intelligence in the future of work. He studied a Ph.D. in computer science at West Virginia University and a master's degree in technological innovation and entrepreneurship at the Monterrey Institute of Technology and Higher Education. He has worked for international organizations such as Google, Microsoft, Amazon, and the United Nations. He was also the technical reviewer on the Artificial Intelligence By Example, Second Edition and Hands-On Explainable AI (XAI) with Python books. He has also built companies that use artificial intelligence in the financial, educational, customer service, and parking industries. Carlos has published numerous research papers, manuscripts, and book chapters for different conferences and journals in his field.
Transformers are a game-changer for Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), which has become one of the pillars of artificial intelligence in a global digital economy.
The global economy has been moving from the physical world to the digital world.
We are witnessing the expansion of social networks versus physical encounters, e-commerce versus physical shopping, digital newspapers, streaming versus physical theaters, remote doctor consultations versus physical visits, remote work instead of on-site tasks, and similar trends in hundreds of more domains.
Artificial intelligence-driven language understanding will continue to expand exponentially, as will the volumes of data these activities generate. Language understanding has become the pillar of language modeling, chatbots, personal assistants, question answering, text summarizing, speech-to-text, sentiment analysis, machine translation, and more.
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