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Key benefits

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

Test transformer models on advanced use cases

Description

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.

Who is this book for?

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 experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

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

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Transformers for Natural Language Processing

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Amazon verified reviews

Thom Ives, Ph.D. Feb 04, 2021

I needed to learn transformers. It was slow going, so I started a learning group on LinkedIn. We were helping each other find sources of how to best learn them.Then Denis Rothman piggybacked on one of my learning posts about transformers, and he made a promise of something big that would soon come out that would help us.Friends at Packt then allowed me to have a prerelease version of this book. Until this book came out, it was hard to not only collect what you needed to learn how transformers work, but also to learn how to use them, transfer learn with them, and when and how you might want to build and train your own transformer(s).After looking through so many sources, I can attest that not only will this book help you get started rapidly with using and training and transfer learning transformers, but it will also help you understand transformers in deep philosophical ways.I am VERY grateful to Denis for writing this book AND FOR writing it very well.

Amazon Verified review

Amazon Customer Mar 24, 2021

The book is a well written summary of using Transformers for Natural language processing and will be a good addition to any Deep learning / NLP library.The book covers most of mathematical background and architectures in practical use.This book is very good for learning and hand on practice, though it does require certain understanding of Deep learning field and concepts.

William Le Clair May 24, 2021

The first chapters of the book are a good introduction to the use of Transformers with detailed explanations BERT, Robert, and OpenAI (GPT-2 & GPT-3) with flows explaing how to select parameters. There is also a chapter that goes through what to consider when constructing datasets for your testing and training to avoid unexpected vocabulary such as profanity. The last chapters introduce you to additional packages that you can use to help you get started faster such as Huggingface, Haystack, and Allenlp which is a great help for getting you started and allowing you to compare results.

Prasad Duvvuri Feb 13, 2021

As an NLP enthusiast looking to advance my knowledge In find this book an excellent resource. This book covers various topics related to NLP in great detail and excellent comprehension.

WU. Apr 19, 2021

Having been impressed by Mr. Rothman's previous work on Explainable AI (X-AI), I jumped at the chance to get my hands on this as I was starting a project on NLP and needed to quickly become acquainted with the latest practical advancements in the field.To that end, this book does not disappoint. It is comprehensive in its treatment of transformers, with plenty of hands-on examples to cement the material. I found the tutorials relevant and easy to follow, with each section touching upon a different use case while building in complexity. This, I think, is the correct way to learn new material.I was able to leverage the contents of this book to build a pretty decent model (with very little fine-tuning) for a proof-of-concept with a prospective client, but sadly the trail went cold before we could present the results. As with everything, data is important and I applaud the author for staying away from the more "vanilla" datasets that are usually fare in these types of books.I will say, however, that parts of the book are a bit difficult without the proper introduction to the theory, so you might find yourself having to supplement with other sources. Mr. Rothman is, at times, too succinct in his treatment of core concepts, leaving the reader to unpack theory while he swiftly moves to the examples. All is well documented, however, so no one should have much trouble locating resources. Thus, it is more of an intermediate-level text in my opinion.Overall, I can recommend this to anyone seeking to implement transformers for NLP.

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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