Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion

by Savaş Yıldırım, Meysam Asgari- Chenaghlu

Address NLP tasks as well as multi-modal tasks including both NLP and CV through the utilization of modern transformer architecture. The Transformer-based language models such as BERT, T5, GPT, DALL-E, ChatGPT have dominated natural language processing studies and become a new paradigm. Understand and be able to implement multimodal solutions including text-to-image. Computer vision solutions that are based on Transformers are also explained in the book. Thanks to their accurate and fast fine-tuning capabilities, Transformer-based language models outperformed traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Apart from NLP, recently a fast-growing area in multimodal learning and generative AI has been established which shows promising results. Dalle and Stable diffusions are examples of it. Developers working with The Transformers architecture will be able to put their knowledge to work with this practical guide to NLP. The book provides a hands-on approach to implementation and associated methodologies in the field of NLP that will have you up-and-running, and productive in no time. Also, developers that want to learn more about multimodal models and generative AI in the field of computer vision can use this book as a source. The book is for deep learning researchers, hands-on practitioners, ML/NLP researchers, educators and their students who have a good command of programming subjects, have knowledge in the field of machine learning and artificial intelligence, and want to develop applications in the field of cutting-edge natural language processing as well as multimodal tasks. The readers will have to know at least python or any programming language, know machine learning literature, have some basic understanding of computer science, as this book is going to cover the practical aspects of natural language processing and multimodal deep learning.

462 pages, Paperback

Published June 3, 2024