Название: Transformers in Action (MEAP v7)
Автор: Nicole Koenigstein
Издательство: Manning Publications
Год: 2024
Страниц: 272
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB
Transformers are the superpower behind large language models (LLMs) like ChatGPT, Bard, and LLAMA. Transformers in Action gives you the insights, practical techniques, and extensive code samples you need to adapt pretrained transformer models to new and exciting tasks. Technically speaking, a “Transformer” is a neural network model that finds relationships in sequences of words or other data by using a mathematical technique called attention in its encoder/decoder components. This setup allows a transformer model to learn context and meaning from even long sequences of text, thus creating much more natural responses and predictions. Understanding the transformers architecture is the key to unlocking the power of LLMs for your own AI applications. This comprehensive guide takes you from the origins of transformers all the way to fine-tuning an LLM for your own projects. Author Nicole Königstein demonstrates the vital mathematical and theoretical background of the transformer architecture practically through executable Jupyter notebooks, illuminating how this amazing technology works in action. Transformers have established themselves as a indispensable tool in the field of Machine Learning and Artificial Intelligence as the research and deployment of Large Language Models (LLMs) continues to expand. This book will take you on a fascinating journey through the applications of Transformers, which have, in recent years, evolved from their initial use in natural language processing (NLP) to a wide array of domains. These include, but is not limited to, computer vision, speech recognition, reinforcement learning, mathematical operations, and the study of biological systems such as protein folding. The most notable innovations have been the emergence of decision Transformers and multimodal models. These groundbreaking models have the potential to reshape our understanding of Deep Learning and broaden its horizons. Readers should be comfortable with the basics of ML, Python, and common data tools.
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