Blochspin (37 results)
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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- Softcover
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- Softcover
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Hardcover
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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- Hardcover
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Paperback. Condition: new. Paperback. This book presents a rigorous framework for selecting high-technology stocks by treating news, product releases, earnings calls, social media, and market narratives as structured sources of investment signal rather than as noise or entertainment. It argues that technology stocks behave diffe…rently from traditional equities because they are often valued on future optionality, platform leverage, network effects, intellectual property, and expected category dominance rather than on current earnings alone. From that foundation, the manuscript develops a full research method for interpreting tech news like an investor: how to distinguish signal from hype, map a single event across an ecosystem of suppliers and beneficiaries, identify durable technology waves early, convert themes into ranked watchlists, time entries with discipline, and build repeatable systems for tracking developments across semiconductors, cloud software, cybersecurity, AI infrastructure, consumer platforms, and other major sectors.Across its later chapters, the book moves from analysis to execution by showing how investors can build an actual operating system for trend-based investing. It covers how to score events, maintain research journals, design dashboards and alerts, monitor social and technical communities, construct resilient portfolios, manage drawdowns, and protect judgment from herd behavior, overconfidence, and narrative compression. It also addresses more advanced issues such as automating information analysis, evaluating online source credibility, and preserving ethical discipline in an age of algorithmic amplification and market theater. The result is a mature guide to modern tech investing that emphasizes process consistency, probabilistic thinking, and intellectual integrity, arguing that long-term success comes not from chasing every headline, but from understanding how technology changes industries and how disciplined investors can position themselves ahead of consensus without becoming victims of noise. A practical guide to high-tech stock investing that shows how to turn tech news, product signals, earnings commentary, and social media trends into a disciplined framework for finding, evaluating, and managing technology stock opportunities. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Hardcover. Condition: new. Hardcover. Marketing in the Age of AI argues that marketing is undergoing a fundamental transformation from a campaign-based communications discipline into a continuously adaptive intelligence system. Across the book, AI is presented not as a mere tool for faster copywriting or better targeting, but as… the infrastructure now shaping how brands generate content, predict customer behavior, orchestrate journeys, allocate media spend, measure incrementality, and govern decision-making. The book traces this shift from machine-mediated distribution and data advantage through generative AI, conversational systems, algorithmic visibility, attribution science, organizational redesign, autonomous media, marketing agents, and the global regulatory landscape, showing how each layer contributes to a new model of competitive power.At its deepest level, the book contends that the future of marketing will belong to organizations that combine technical sophistication with strategic coherence, causal discipline, and institutional trust. It explains how predictive models, reinforcement logic, attention economics, and bounded automation can improve performance, but it also insists that long-term advantage depends on governance, legitimacy, and the ability to use intelligence responsibly. By the end, marketing emerges not as a collection of channels or campaigns, but as a system for sensing markets, acting under uncertainty, learning from feedback, and building enduring customer relationships in a world increasingly shaped by AI. A strategic and technically informed examination of how artificial intelligence is transforming marketing into an adaptive intelligence system driven by prediction, automation, causal measurement, customer experience, and trust. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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£ 83.71
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Hardcover. Condition: new. Hardcover. Marketing in the Age of AI argues that marketing is undergoing a fundamental transformation from a campaign-based communications discipline into a continuously adaptive intelligence system. Across the book, AI is presented not as a mere tool for faster copywriting or better targeting, but as… the infrastructure now shaping how brands generate content, predict customer behavior, orchestrate journeys, allocate media spend, measure incrementality, and govern decision-making. The book traces this shift from machine-mediated distribution and data advantage through generative AI, conversational systems, algorithmic visibility, attribution science, organizational redesign, autonomous media, marketing agents, and the global regulatory landscape, showing how each layer contributes to a new model of competitive power.At its deepest level, the book contends that the future of marketing will belong to organizations that combine technical sophistication with strategic coherence, causal discipline, and institutional trust. It explains how predictive models, reinforcement logic, attention economics, and bounded automation can improve performance, but it also insists that long-term advantage depends on governance, legitimacy, and the ability to use intelligence responsibly. By the end, marketing emerges not as a collection of channels or campaigns, but as a system for sensing markets, acting under uncertainty, learning from feedback, and building enduring customer relationships in a world increasingly shaped by AI. A strategic and technically informed examination of how artificial intelligence is transforming marketing into an adaptive intelligence system driven by prediction, automation, causal measurement, customer experience, and trust. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Hardcover
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Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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£ 83.71
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Hardcover
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Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 83.71
£ 27.43 shippingShips from Australia to U.S.A.Quantity: 1 available
Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
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Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 83.71
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Paperback. Condition: new. Paperback. This book presents a rigorous framework for selecting high-technology stocks by treating news, product releases, earnings calls, social media, and market narratives as structured sources of investment signal rather than as noise or entertainment. It argues that technology stocks behave diffe…rently from traditional equities because they are often valued on future optionality, platform leverage, network effects, intellectual property, and expected category dominance rather than on current earnings alone. From that foundation, the manuscript develops a full research method for interpreting tech news like an investor: how to distinguish signal from hype, map a single event across an ecosystem of suppliers and beneficiaries, identify durable technology waves early, convert themes into ranked watchlists, time entries with discipline, and build repeatable systems for tracking developments across semiconductors, cloud software, cybersecurity, AI infrastructure, consumer platforms, and other major sectors.Across its later chapters, the book moves from analysis to execution by showing how investors can build an actual operating system for trend-based investing. It covers how to score events, maintain research journals, design dashboards and alerts, monitor social and technical communities, construct resilient portfolios, manage drawdowns, and protect judgment from herd behavior, overconfidence, and narrative compression. It also addresses more advanced issues such as automating information analysis, evaluating online source credibility, and preserving ethical discipline in an age of algorithmic amplification and market theater. The result is a mature guide to modern tech investing that emphasizes process consistency, probabilistic thinking, and intellectual integrity, arguing that long-term success comes not from chasing every headline, but from understanding how technology changes industries and how disciplined investors can position themselves ahead of consensus without becoming victims of noise. A practical guide to high-tech stock investing that shows how to turn tech news, product signals, earnings commentary, and social media trends into a disciplined framework for finding, evaluating, and managing technology stock opportunities. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Hardcover
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Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 83.71
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 78.49
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. This book presents a rigorous framework for selecting high-technology stocks by treating news, product releases, earnings calls, social media, and market narratives as structured sources of investment signal rather than as noise or entertainment. It argues that technology stocks behave diffe…rently from traditional equities because they are often valued on future optionality, platform leverage, network effects, intellectual property, and expected category dominance rather than on current earnings alone. From that foundation, the manuscript develops a full research method for interpreting tech news like an investor: how to distinguish signal from hype, map a single event across an ecosystem of suppliers and beneficiaries, identify durable technology waves early, convert themes into ranked watchlists, time entries with discipline, and build repeatable systems for tracking developments across semiconductors, cloud software, cybersecurity, AI infrastructure, consumer platforms, and other major sectors.Across its later chapters, the book moves from analysis to execution by showing how investors can build an actual operating system for trend-based investing. It covers how to score events, maintain research journals, design dashboards and alerts, monitor social and technical communities, construct resilient portfolios, manage drawdowns, and protect judgment from herd behavior, overconfidence, and narrative compression. It also addresses more advanced issues such as automating information analysis, evaluating online source credibility, and preserving ethical discipline in an age of algorithmic amplification and market theater. The result is a mature guide to modern tech investing that emphasizes process consistency, probabilistic thinking, and intellectual integrity, arguing that long-term success comes not from chasing every headline, but from understanding how technology changes industries and how disciplined investors can position themselves ahead of consensus without becoming victims of noise. A practical guide to high-tech stock investing that shows how to turn tech news, product signals, earnings commentary, and social media trends into a disciplined framework for finding, evaluating, and managing technology stock opportunities. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 78.49
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 91.99
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Hardcover. Condition: new. Hardcover. Marketing in the Age of AI argues that marketing is undergoing a fundamental transformation from a campaign-based communications discipline into a continuously adaptive intelligence system. Across the book, AI is presented not as a mere tool for faster copywriting or better targeting, but as… the infrastructure now shaping how brands generate content, predict customer behavior, orchestrate journeys, allocate media spend, measure incrementality, and govern decision-making. The book traces this shift from machine-mediated distribution and data advantage through generative AI, conversational systems, algorithmic visibility, attribution science, organizational redesign, autonomous media, marketing agents, and the global regulatory landscape, showing how each layer contributes to a new model of competitive power.At its deepest level, the book contends that the future of marketing will belong to organizations that combine technical sophistication with strategic coherence, causal discipline, and institutional trust. It explains how predictive models, reinforcement logic, attention economics, and bounded automation can improve performance, but it also insists that long-term advantage depends on governance, legitimacy, and the ability to use intelligence responsibly. By the end, marketing emerges not as a collection of channels or campaigns, but as a system for sensing markets, acting under uncertainty, learning from feedback, and building enduring customer relationships in a world increasingly shaped by AI. A strategic and technically informed examination of how artificial intelligence is transforming marketing into an adaptive intelligence system driven by prediction, automation, causal measurement, customer experience, and trust. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 92.49
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Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 102.99
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Buch. Condition: Neu. Marketing in the Age of AI | The Intelligent Influence Machine | Alena Heinsohn | Buch | Englisch | 2026 | BlochSpin | EAN 9798904175573 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condition: Neu. Maximizing High-Tech Stock Picks Through Tech News, Trends, and Social Media Intelligence | Andrew Jeremy | Taschenbuch | Englisch | 2026 | BlochSpin | EAN 9798904175634 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print o…n Demand.
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Buch. Condition: Neu. Foundations of Intelligent MRI | From Spin Physics to AI Models | Andrew Kiruluta | Buch | Englisch | 2026 | BlochSpin | EAN 9798904174897 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
