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Laatst bijgewerkt op 24 mei 2024 17:46:16 CESTAlle herzieningen bekijkenAlle herzieningen bekijken

Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
Weight
100grams
Subjects
N/A
Pages
384
Size
23.5 x 19.1 x 2 centimeters (0.3
Topic
Unknown
Book Title
Transformers for Natural Language Processing: Build innovative d
ISBN
9781800565791
Publication Year
2021
Type
Textbook
Format
Paperback
Language
English
Publication Name
Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
Item Height
93mm
Author
Denis Rothman
Publisher
Packt Publishing
Item Width
75mm
Subject
Computer Science
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 DescriptionThe 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 forSince 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
ISBN-13
9781800565791
eBay Product ID (ePID)
16046672371

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
Paperback
Language
English
Subject
Computer Science
Publication Year
2021
Type
Textbook
Number of Pages
384 Pages

Dimensions

Item Height
93mm
Item Width
75mm

Additional Product Features

Title_Author
Denis Rothman
Country/Region of Manufacture
United Kingdom

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KVK-nummer:
  • 06386895
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