ISBN: 9798250766623 - physics of ai & machine learning: from fundamental principles to cutting-edge technologies (9 risultati)

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798250766623

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    Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798250766623

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    Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798250766623

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    Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK

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    EUR 15,70

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  • Lingua: Inglese

    Editore: Independently published, 2026

    9798250766623

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    Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK

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    Condizione: Usato - Come nuovo

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    Condizione: As New. Unread book in perfect condition.

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798250766623

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    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

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    Paperback. Condizione: new. Paperback. Artificial Intelligence is often explained through algorithms, code, and data. But beneath every neural network, every learning algorithm, and every AI model lies something deeper - the fundamental laws of physics.The Physics Behind AI Hardware and Neural Networks reveals the hidden physical principles that make modern artificial intelligence possible. From semiconductor physics and quantum mechanics to statistical mechanics and nonlinear dynamics, this book bridges the gap between AI, physics, and advanced computation.Unlike traditional AI books that focus only on software and programming, this book explores how the physical world enables intelligence in machines.Readers will discover how: - Semiconductor physics and MOSFET transistors power modern AI accelerators- CMOS technology and neuromorphic chips mimic the brain's computational architecture- Photonic computing uses light to perform ultra-fast neural network calculations- Quantum computing and quantum optics open new frontiers for AI algorithms- Statistical mechanics and information theory explain how neural networks learn- Optimization and thermodynamics shape training algorithms like gradient descent and simulated annealing- Dynamical systems and chaos theory influence deep learning stability- Energy landscapes and free energy principles connect physics with machine intelligenceThe book also explores the groundbreaking work behind modern AI, including Hopfield networks, Boltzmann machines, and deep learning, along with the 2024 Nobel Prize in Physics contributions to neural network theory.Moving beyond theory, the final chapters explore how AI is transforming physics research itself, from high-energy particle physics to material discovery and quantum technologies.What You Will Learn- The physics of AI hardware and semiconductor devices- How statistical mechanics explains deep learning- The role of information theory in neural networks- Physics-based perspectives on optimization and learning algorithms- Neuromorphic, photonic, and quantum computing architectures for AI- The connection between energy landscapes, chaos, and training dynamics- How AI is accelerating discoveries in modern physicsWho This Book Is ForThis book is ideal for: - Physics students curious about artificial intelligence- AI researchers interested in the fundamental science behind machine learning- Engineers working on AI hardware and accelerators- Researchers exploring quantum computing, photonics, and neuromorphic systems- Anyone fascinated by the intersection of physics, computation, and intelligenceArtificial intelligence is not just software - it is a physical phenomenon governed by the laws of nature.Understanding these laws may be the key to building the next generation of intelligent machines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798250766623

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    EUR 16,34

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    PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently published, 2026

    9798250766623

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    Da: California Books, Miami, FL, U.S.A.California Books

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    EUR 15,60

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  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798250766623

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    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    Condizione: Nuovo

    EUR 15,71

    EUR 3,94 spedizione 
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    Quantità: Più di 20 disponibili

    PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798250766623

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    • Print on Demand

    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

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    Condizione: Nuovo

    EUR 19,73

    EUR 44,32 spedizione 
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    Quantità: 1 disponibile

    Paperback. Condizione: new. Paperback. Artificial Intelligence is often explained through algorithms, code, and data. But beneath every neural network, every learning algorithm, and every AI model lies something deeper - the fundamental laws of physics.The Physics Behind AI Hardware and Neural Networks reveals the hidden physical principles that make modern artificial intelligence possible. From semiconductor physics and quantum mechanics to statistical mechanics and nonlinear dynamics, this book bridges the gap between AI, physics, and advanced computation.Unlike traditional AI books that focus only on software and programming, this book explores how the physical world enables intelligence in machines.Readers will discover how: - Semiconductor physics and MOSFET transistors power modern AI accelerators- CMOS technology and neuromorphic chips mimic the brain's computational architecture- Photonic computing uses light to perform ultra-fast neural network calculations- Quantum computing and quantum optics open new frontiers for AI algorithms- Statistical mechanics and information theory explain how neural networks learn- Optimization and thermodynamics shape training algorithms like gradient descent and simulated annealing- Dynamical systems and chaos theory influence deep learning stability- Energy landscapes and free energy principles connect physics with machine intelligenceThe book also explores the groundbreaking work behind modern AI, including Hopfield networks, Boltzmann machines, and deep learning, along with the 2024 Nobel Prize in Physics contributions to neural network theory.Moving beyond theory, the final chapters explore how AI is transforming physics research itself, from high-energy particle physics to material discovery and quantum technologies.What You Will Learn- The physics of AI hardware and semiconductor devices- How statistical mechanics explains deep learning- The role of information theory in neural networks- Physics-based perspectives on optimization and learning algorithms- Neuromorphic, photonic, and quantum computing architectures for AI- The connection between energy landscapes, chaos, and training dynamics- How AI is accelerating discoveries in modern physicsWho This Book Is ForThis book is ideal for: - Physics students curious about artificial intelligence- AI researchers interested in the fundamental science behind machine learning- Engineers working on AI hardware and accelerators- Researchers exploring quantum computing, photonics, and neuromorphic systems- Anyone fascinated by the intersection of physics, computation, and intelligenceArtificial intelligence is not just software - it is a physical phenomenon governed by the laws of nature.Understanding these laws may be the key to building the next generation of intelligent machines. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…