Isbn: 9786209598456 - advanced deep learning systems from theory to deployment (11 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Taschenbuch. Condizione: Neu. ADVANCED DEEP LEARNING SYSTEMS FROM THEORY TO DEPLOYMENT | Geetha C (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209598456 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Condizione: New.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 136 pp. Englisch.

Paperback. Condizione: new. Paperback. Advanced deep learning systems represent some of the most sophisticated technical achievements of our era, combining theoretical insights from neuroscience and mathematics, practical engineering from distributed computing and systems design, and domain expertise from countless application areas. Building such systems requires combining knowledge across multiple disciplines-understanding not just neural networks but also optimization, distributed systems, software engineering, data management, and domain-specific challenges.The field remains young with tremendous opportunity for innovation and impact. Models that once seemed impossible to train now train routinely. Deployments at scales unimaginable a few years ago now operate reliably. Applications that were pure science fiction now benefit billions of users. Yet enormous challenges remain-building systems that are efficient enough for edge deployment, fair enough to avoid amplifying societal biases, robust enough to handle distribution shifts and adversarial inputs, and interpretable enough to enable understanding 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.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Advanced deep learning systems represent some of the most sophisticated technical achievements of our era, combining theoretical insights from neuroscience and mathematics, practical engineering from distributed computing and systems design, and domain expertise from countless application areas. Building such systems requires combining knowledge across multiple disciplines-understanding not just neural networks but also optimization, distributed systems, software engineering, data management, and domain-specific challenges.The field remains young with tremendous opportunity for innovation and impact. Models that once seemed impossible to train now train routinely. Deployments at scales unimaginable a few years ago now operate reliably. Applications that were pure science fiction now benefit billions of users. Yet enormous challenges remain-building systems that are efficient enough for edge deployment, fair enough to avoid amplifying societal biases, robust enough to handle distribution shifts and adversarial inputs, and interpretable enough to enable understanding and trust.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advanced deep learning systems represent some of the most sophisticated technical achievements of our era, combining theoretical insights from neuroscience and mathematics, practical engineering from distributed computing and systems design, and domain expertise from countless application areas. Building such systems requires combining knowledge across multiple disciplines-understanding not just neural networks but also optimization, distributed systems, software engineering, data management, and domain-specific challenges.The field remains young with tremendous opportunity for innovation and impact. Models that once seemed impossible to train now train routinely. Deployments at scales unimaginable a few years ago now operate reliably. Applications that were pure science fiction now benefit billions of users. Yet enormous challenges remain-building systems that are efficient enough for edge deployment, fair enough to avoid amplifying societal biases, robust enough to handle distribution shifts and adversarial inputs, and interpretable enough to enable understanding and trust.…