Transformers in Action, Koenigstein N., Serrano L., 2026

Transformers in Action, Koenigstein N., Serrano L., 2026.
        
   Transformers in Action is a comprehensive guide to understanding and applying transformer models in the language and multimodal space. These models are foundational to modern AI systems such as ChatGPT and Gemini. The book aims to provide you with a solid foundation to use these models for your own projects, starting with the core concepts of transformers and then moving to practical and more advanced applications such as multimodal retrieval systems.




Foundations of modern transformer models.
Since their introduction at the 31st Conference on Neural Information Processing Systems (NIPS 2017), transformers have become a popular topic of interest. The transformer is a type of machine learning model that, unlike its predecessors, can understand the context of words in a sentence by focusing on different parts of the sentence simultaneously. Exactly this fact makes transformers highly efficient variety of natural language processing (NLP) tasks, including text classification, summarization, and generation. They are being extensively used across many applications, underscoring the importance of understanding these models in full depth.

While you may be eager to get started and learn how to build your own ChatGPT model that can answer any questions you might have and even make predictions, it is critical to first grasp the principles of a transformer model. This is because, in order to be successful in this field and to use more advanced models, you must first establish a solid foundation.

Contents.
Foreword.
Preface.
Acknowledgments.
About this book.
About the author.
About the cover illustration.
PART 1 FOUNDATIONS OF MODERN TRANSFORMER MODELS.
1. The need for transformers.
2. A deeper look into transformers.
PART 2 GENERATIVE TRANSFORMERS.
3. Model families and architecture variants.
4. Text generation strategies and prompting techniques.
5. Preference alignment and retrieval-augmented generation.
PART 3 SPECIALIZED MODELS.
6. Multimodal models.
7. Efficient and specialized small language models.
8. Training and evaluating large language models.
9. Optimizing and scaling large language models.
10. Ethical and responsible large language models.
References.
Index.



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Теги: учебник по программированию :: программирование :: Koenigstein :: Serrano