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Denis Rothman Transformers for Natural Language Processi (Paperback) (UK IMPORT)

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Specificaties

Objectstaat
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Book Title
Transformers for Natural Language Processing
Publication Name
Transformers for Natural Language Processing : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More
Title
Transformers for Natural Language Processing
Subtitle
Build innovative deep neural network architectures for NLP with P
Author
Denis Rothman
Format
Trade Paperback
ISBN-10
1800565798
EAN
9781800565791
ISBN
9781800565791
Publisher
Packt Publishing, The Limited
Genre
Computing & Internet
Release Date
29/01/2021
Release Year
2021
Language
English
Country/Region of Manufacture
GB
Item Height
93mm
Item Length
92.5 in
Publication Year
2021
Type
Textbook
Subject Area
Computers
Item Width
75 in
Subject
Natural Language Processing, Neural Networks, Intelligence (Ai) & Semantics
Number of Pages
384 Pages

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Product Information

Take your NLP knowledge to the next level and 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 Test transformer models on advanced use cases Book 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. 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 Who this book is 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.

Product Identifiers

Publisher
Packt Publishing, The Limited
ISBN-10
1800565798
ISBN-13
9781800565791
eBay Product ID (ePID)
16050385494

Product Key Features

Author
Denis Rothman
Publication Name
Transformers for Natural Language Processing : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More
Format
Trade Paperback
Language
English
Subject
Natural Language Processing, Neural Networks, Intelligence (Ai) & Semantics
Publication Year
2021
Type
Textbook
Subject Area
Computers
Number of Pages
384 Pages

Dimensions

Item Length
92.5 in
Item Width
75 in

Additional Product Features

Table of Content
Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers (N.B. Please use the Look Inside option to see further chapters)
Target Audience
Trade

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