Transformers: The Definitive Guide: Applications Beyond NLP
Published May 5th, 2026
ISBN 1098167015
Language English
Pages 370
Сategories Computers, Internet • Education, Learning • Engineering, Technology • Programming, Python
Formats EPUB
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The vast potential of AI technology remains untapped in areas like audio, video, and complex data analysis. In fact, many of today's professionals find it challenging to apply AI innovations across these diverse domains due to a lack of guidance and practical implementations.
This comprehensive guide, tailored especially for intermediate to advanced ML engineers, data scientists, and researchers, fills the gap. Author Nicole Koenigstein guides readers through the versatile applications of transformer models, not only deepening theoretical understanding but also emphasizing actionable strategies for real-world applications. The book equips you with a grand unified theory for transformers—foundational insights that keep you on the cutting edge regardless of state-of-the-art model evolutions.
You'll discover how to apply:
• Transformers in nontext domains like image, video, and music generation
• Reasoning models, coding agents, and multi-agent architectures
• Training-time and test-time optimization strategies
• Production deployment, runtime engineering, and hardware efficiency
This comprehensive guide, tailored especially for intermediate to advanced ML engineers, data scientists, and researchers, fills the gap. Author Nicole Koenigstein guides readers through the versatile applications of transformer models, not only deepening theoretical understanding but also emphasizing actionable strategies for real-world applications. The book equips you with a grand unified theory for transformers—foundational insights that keep you on the cutting edge regardless of state-of-the-art model evolutions.
You'll discover how to apply:
• Transformers in nontext domains like image, video, and music generation
• Reasoning models, coding agents, and multi-agent architectures
• Training-time and test-time optimization strategies
• Production deployment, runtime engineering, and hardware efficiency
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