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Introduction to Transformers for NLP

35,30 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 182f64e180b4e5418591a803e08ad9fafd
Autor: Jain, Shashank Mohan
Themengebiete: Artificial Intelligence Deep learning Hugging Face Machine Learning Natural Language Processing Natural Language Understanding Python Transformers
Veröffentlichungsdatum: 21.10.2022
EAN: 9781484288436
Sprache: Englisch
Seitenzahl: 165
Produktart: Kartoniert / Broschiert
Verlag: APRESS
Untertitel: With the Hugging Face Library and Models to Solve Problems
Produktinformationen "Introduction to Transformers for NLP"
Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing.This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation.After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library.What You Will LearnUnderstand language models and their importance in NLP and NLU (Natural Language Understanding)Master Transformer architecture through practical examplesUse the Hugging Face library in Transformer-based language modelsCreate a simple code generator in Python based on Transformer architectureWho This Book Is ForData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)

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