Transformers in Deep Learning Architecture

Name: Transformers in Deep Learning Architecture
Brand: Gandhi
SKU: 07169497

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7553815Transformers in Deep Learning Architecturehttps://www.gandhi.com.mx/transformers-in-deep-learning-architecture-6610000817047/phttps://gandhi.vtexassets.com/arquivos/ids/7174364/image.jpg?v=638844133254500000189189MXNGandhiInStock/Ebooks/"Transformers in Deep Learning Architecture""Transformers in Deep Learning Architecture" presents a comprehensive and rigorous exploration of the transformer paradigmthe foundational architecture that has revolutionized modern artificial intelligence. The book opens by situating transformers within the historical context of neural sequence models, methodically tracing their evolution from recurrent neural networks to the self-attention mechanisms that address their predecessors limitations. Early chapters lay a strong mathematical and conceptual foundation, introducing key terminology, theoretical principles, and detailed comparisons with alternative architectures to prepare readers for a deep technical dive.At its core, the book delivers an in-depth analysis of the architectural details and operational intricacies that underpin transformer models. Subsequent chapters dissect the encoder-decoder framework, decompose self-attention and multi-head attention mechanisms, and discuss design choices such as positional encodings, feedforward networks, normalization strategies, and scaling laws. Readers also encounter a nuanced treatment of advanced attention variantsincluding efficient, sparse, and cross-modal extensionsalong with proven paradigms for pretraining, transfer learning, and domain adaptation. Rich case studies illustrate the extraordinary performance of transformers in natural language processing, vision, audio, and multimodal tasks, highlighting both established applications and emerging frontiers.Beyond technical mastery, the book addresses the practical dimensions and responsible deployment of large transformer models. It guides practitioners through scalable training, distributed modernization, and infrastructure optimization, while confronting contemporary challenges in interpretability, robustness, ethics, and privacy. The final chapters forecast the transformative future of the field with discussions on long-context modeling, symbolic integration, neuromorphic and quantum-inspired approaches, and the profound societal implications of widespread transformer adoption. Altogether, this volume stands as both an authoritative reference and a visionary roadmap for researchers and engineers working at the cutting edge of deep learning....7169497Transformers in Deep Learning Architecture189189https://www.gandhi.com.mx/transformers-in-deep-learning-architecture-6610000817047/phttps://gandhi.vtexassets.com/arquivos/ids/7174364/image.jpg?v=638844133254500000InStockMXN99999DIEbook20256610000817047_W3siaWQiOiJmNzhhMzAwMS04YjRlLTQ5YzItOGU3NS0zYWFjMzU4YWY4NzMiLCJsaXN0UHJpY2UiOjE4OSwiZGlzY291bnQiOjAsInNlbGxpbmdQcmljZSI6MTg5LCJpbmNsdWRlc1RheCI6dHJ1ZSwicHJpY2VUeXBlIjoiSXBwIiwiY3VycmVuY3kiOiJNWE4iLCJmcm9tIjoiMjAyNS0wNi0xMlQxNzowMDowMFoiLCJyZWdpb24iOiJNWCIsImlzUHJlb3JkZXIiOmZhbHNlfV0=6610000817047_"Transformers in Deep Learning Architecture""Transformers in Deep Learning Architecture" presents a comprehensive and rigorous exploration of the transformer paradigmthe foundational architecture that has revolutionized modern artificial intelligence. The book opens by situating transformers within the historical context of neural sequence models, methodically tracing their evolution from recurrent neural networks to the self-attention mechanisms that address their predecessors limitations. Early chapters lay a strong mathematical and conceptual foundation, introducing key terminology, theoretical principles, and detailed comparisons with alternative architectures to prepare readers for a deep technical dive.At its core, the book delivers an in-depth analysis of the architectural details and operational intricacies that underpin transformer models. Subsequent chapters dissect the encoder-decoder framework, decompose self-attention and multi-head attention mechanisms, and discuss design choices such as positional encodings, feedforward networks, normalization strategies, and scaling laws. Readers also encounter a nuanced treatment of advanced attention variantsincluding efficient, sparse, and cross-modal extensionsalong with proven paradigms for pretraining, transfer learning, and domain adaptation. Rich case studies illustrate the extraordinary performance of transformers in natural language processing, vision, audio, and multimodal tasks, highlighting both established applications and emerging frontiers.Beyond technical mastery, the book addresses the practical dimensions and responsible deployment of large transformer models. It guides practitioners through scalable training, distributed modernization, and infrastructure optimization, while confronting contemporary challenges in interpretability, robustness, ethics, and privacy. The final chapters forecast the transformative future of the field with discussions on long-context modeling, symbolic integration, neuromorphic and quantum-inspired approaches, and the profound societal implications of widespread transformer adoption. Altogether, this volume stands as both an authoritative reference and a visionary roadmap for researchers and engineers working at the cutting edge of deep learning....6610000817047_HiTeX Presslibro_electonico_6610000817047_6610000817047Richard JohnsonInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/content2connect_drm-epub-2e49a2ed-54f1-41c9-8f43-26e222f7f799.epub2025-05-28T00:00:00+00:00HiTeX Press