Mastering Transformers
Explore the accurate and fast fine-tuning capabilities of transformer-based language models and understand how they outperform traditional machine learning-based approaches when solving challenging NLU problems. Developers working with the Transformers architecture will be able to put their knowledge to work with this practical guide.
Offered by
Difficulty Level
Intermediate
Completion Time
12h28m
Language
English
About Book
Who Is This Book For?
This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.
Mastering Transformers
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h28m total length
From Bag-of-Words to the Transformers
A Hands-On Introduction to the Subject
Autoencoding Language Models
Autoregressive and Other Language Models
Fine-Tuning Language Models for Text Classification
Fine-Tuning Language Models for Token Classification
Text Representation
Working with Efficient Transformers
Cross-Lingual and Multilingual Language Modeling
Serving Transformer Models
Attention Visualization and Experiment Tracking
Related Resources
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