finelybook
Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python,PyTorch,TensorFlow,BERT,RoBERTa,and more
by Denis Rothman
Publisher Finelybook 出版社: Packt Publishing (January 29,2021)
Language 语言: English
pages 页数: 384 pages
ISBN-10 书号: 1800565798
ISBN-13 书号: 9781800565791
Book Description
Become an AI language understanding expert by 作者: mastering the quantum leap of Transformer neural network models
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 作者: 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 作者: 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
