Natural Language Processing with Transformers, Revised Edition by Leandro von...

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Specifiche dell'oggetto

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Ottime condizioni: Libro che non sembra nuovo ed è già stato letto, ma è in condizioni eccellenti. ...
Features
Revised Edition
ISBN
9781098136796
Categoria

Informazioni su questo prodotto

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1098136799
ISBN-13
9781098136796
eBay Product ID (ePID)
17057249513

Product Key Features

Number of Pages
406 Pages
Language
English
Publication Name
Natural Language Processing with Transformers, Revised Edition
Subject
Intelligence (Ai) & Semantics, Natural Language Processing, Data Processing
Publication Year
2022
Type
Textbook
Author
Leandro Von Werra, Lewis Tunstall, Thomas Wolf
Subject Area
Computers
Format
Trade Paperback

Dimensions

Item Height
1.1 in
Item Weight
24.4 Oz
Item Length
9.1 in
Item Width
7 in

Additional Product Features

LCCN
2023-275986
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/5
Synopsis
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
LC Classification Number
QA76.9.N38

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