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Foundational Models and Architectures S1. Generative AI, #1
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- FormatePub
- ISBN8231273447
- EAN9798231273447
- Date de parution08/06/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
Foundational Models and Architectures by @Leaster Starx offers a rigorous and comprehensive exploration of the evolving landscape of generative artificial intelligence. This academic volume delves into the theoretical principles, architectural innovations, and real-world applications driving the next generation of AI models. Through meticulously crafted chapters, it analyzes state-of-the-art systems such as diffusion models, transformers, multimodal networks, and neural radiance fields (NeRF), presenting their design, performance, and societal implications.
The book systematically unpacks core concepts including self-supervised learning, probabilistic generative modeling, and emergent zero/few-shot learning capabilities. It evaluates architectural paradigms like GANs, VAEs, and autoregressive models through standardized benchmarks-addressing challenges in scalability, bias, training efficiency, and ethical deployment. Emphasizing the interplay between theory and practice, this volume highlights how foundational models are revolutionizing domains like video synthesis, scientific visualization, personalized education, and creative content generation.
From latent diffusion and transformer-based real-time video models to cross-modal generation systems integrating text, image, and audio, each chapter illuminates critical advances that bridge deep learning research and impactful deployment. The book also addresses advanced frontiers such as continual learning, quantum-enhanced training, and energy-efficient model design, offering readers a panoramic view of the technological, methodological, and philosophical dimensions shaping generative AI.
Designed for researchers, engineers, and students, it equips readers with both the depth and breadth to understand and contribute to the future of intelligent generative systems. Whether exploring the mathematical formulations behind neural architectures, evaluating the real-world implications of generative outputs, or proposing paths toward sustainable and ethical AI, Foundational Models and Architectures stands as a definitive reference for those invested in the future of AI creativity and cognition.
The book systematically unpacks core concepts including self-supervised learning, probabilistic generative modeling, and emergent zero/few-shot learning capabilities. It evaluates architectural paradigms like GANs, VAEs, and autoregressive models through standardized benchmarks-addressing challenges in scalability, bias, training efficiency, and ethical deployment. Emphasizing the interplay between theory and practice, this volume highlights how foundational models are revolutionizing domains like video synthesis, scientific visualization, personalized education, and creative content generation.
From latent diffusion and transformer-based real-time video models to cross-modal generation systems integrating text, image, and audio, each chapter illuminates critical advances that bridge deep learning research and impactful deployment. The book also addresses advanced frontiers such as continual learning, quantum-enhanced training, and energy-efficient model design, offering readers a panoramic view of the technological, methodological, and philosophical dimensions shaping generative AI.
Designed for researchers, engineers, and students, it equips readers with both the depth and breadth to understand and contribute to the future of intelligent generative systems. Whether exploring the mathematical formulations behind neural architectures, evaluating the real-world implications of generative outputs, or proposing paths toward sustainable and ethical AI, Foundational Models and Architectures stands as a definitive reference for those invested in the future of AI creativity and cognition.
Foundational Models and Architectures by @Leaster Starx offers a rigorous and comprehensive exploration of the evolving landscape of generative artificial intelligence. This academic volume delves into the theoretical principles, architectural innovations, and real-world applications driving the next generation of AI models. Through meticulously crafted chapters, it analyzes state-of-the-art systems such as diffusion models, transformers, multimodal networks, and neural radiance fields (NeRF), presenting their design, performance, and societal implications.
The book systematically unpacks core concepts including self-supervised learning, probabilistic generative modeling, and emergent zero/few-shot learning capabilities. It evaluates architectural paradigms like GANs, VAEs, and autoregressive models through standardized benchmarks-addressing challenges in scalability, bias, training efficiency, and ethical deployment. Emphasizing the interplay between theory and practice, this volume highlights how foundational models are revolutionizing domains like video synthesis, scientific visualization, personalized education, and creative content generation.
From latent diffusion and transformer-based real-time video models to cross-modal generation systems integrating text, image, and audio, each chapter illuminates critical advances that bridge deep learning research and impactful deployment. The book also addresses advanced frontiers such as continual learning, quantum-enhanced training, and energy-efficient model design, offering readers a panoramic view of the technological, methodological, and philosophical dimensions shaping generative AI.
Designed for researchers, engineers, and students, it equips readers with both the depth and breadth to understand and contribute to the future of intelligent generative systems. Whether exploring the mathematical formulations behind neural architectures, evaluating the real-world implications of generative outputs, or proposing paths toward sustainable and ethical AI, Foundational Models and Architectures stands as a definitive reference for those invested in the future of AI creativity and cognition.
The book systematically unpacks core concepts including self-supervised learning, probabilistic generative modeling, and emergent zero/few-shot learning capabilities. It evaluates architectural paradigms like GANs, VAEs, and autoregressive models through standardized benchmarks-addressing challenges in scalability, bias, training efficiency, and ethical deployment. Emphasizing the interplay between theory and practice, this volume highlights how foundational models are revolutionizing domains like video synthesis, scientific visualization, personalized education, and creative content generation.
From latent diffusion and transformer-based real-time video models to cross-modal generation systems integrating text, image, and audio, each chapter illuminates critical advances that bridge deep learning research and impactful deployment. The book also addresses advanced frontiers such as continual learning, quantum-enhanced training, and energy-efficient model design, offering readers a panoramic view of the technological, methodological, and philosophical dimensions shaping generative AI.
Designed for researchers, engineers, and students, it equips readers with both the depth and breadth to understand and contribute to the future of intelligent generative systems. Whether exploring the mathematical formulations behind neural architectures, evaluating the real-world implications of generative outputs, or proposing paths toward sustainable and ethical AI, Foundational Models and Architectures stands as a definitive reference for those invested in the future of AI creativity and cognition.
