ISBN: 9781009709033 - generative ai and stochastic thermodynamics: a tale of free energies (10 Ergebnisse)

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  • Sprache: Englisch

    Verlag: Cambridge University Press, 2026

    1009709038 / 9781009709033

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  • Sprache: Englisch

    Verlag: Cambridge University Press, 2026

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    Sprache: Englisch

    Verlag: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Zustand: New. Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.…

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    Sprache: Englisch

    Verlag: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Zustand: New. Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.…

  • Sprache: Englisch

    Verlag: Cambridge University Press, 2026

    1009709038 / 9781009709033

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  • Sprache: Englisch

    Verlag: Cambridge University Press, 2026

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    Paperback. Zustand: Brand New. 307 pages. 6.69x0.66x9.61 inches. In Stock.

  • Sprache: Englisch

    Verlag: Cambridge University Press, 2026

    1009709038 / 9781009709033

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    Sprache: Englisch

    Verlag: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Zustand: New. Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.…

  • Sprache: Englisch

    Verlag: Cambridge University Press Aug 2026, 2026

    1009709038 / 9781009709033

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    Taschenbuch. Zustand: Neu. Neuware - Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.…

  • Weitere Bilder

    Sprache: Englisch

    Verlag: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

    • Softcover

    Anbieter: Rarewaves.com UK, London, Vereinigtes KönigreichRarewaves.com UK

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    Paperback. Zustand: New. Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.…