9783384283375 - optimization algorithms for machine learning: theory and practice de prashad (9 resultados)
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 19,45
Envío por EUR 2,30Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 21,83
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 5 disponibles
Paperback or Softback. Condición: New. Optimization Algorithms for Machine Learning: Theory and Practice. Book.
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 21,24
Envío por EUR 2,30Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 28,86
Envío por EUR 17,28Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 32,75
Envío por EUR 17,28Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
Más imágenes- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 31,23
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Optimization Algorithms for Machine Learning: Theory and Practice | Prashad | Taschenbuch | Englisch | 2024 | tredition | EAN 9783384283375 | Verantwortliche Person für die EU: tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 31,23
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the realm of machine learning, optimization algorithms play a pivotal role in refining models for optimal performance. These algorithms, ranging from classic gradient descent to advanced techniques like stochastic gradient descen…t (SGD), Adam, and RMSprop, are fundamental in minimizing the error function and enhancing model accuracy. Each algorithm offers unique advantages: SGD efficiently handles large datasets by updating parameters iteratively, while Adam adapts learning rates dynamically based on gradient variance.Theoretical understanding of optimization algorithms involves comprehending concepts like convexity, convergence criteria, and the impact of learning rate adjustments. Practically, implementing these algorithms requires tuning hyperparameters and balancing computational efficiency with model effectiveness. Moreover, recent advancements such as meta-heuristic algorithms (e.g., genetic algorithms) expand optimization capabilities for complex, non-convex problems.Mastering optimization algorithms equips practitioners with the tools to improve model robustness and scalability across diverse applications, ensuring machine learning systems perform optimally in real-world scenarios. 338 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 31,23
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the realm of machine learning, optimization algorithms play a pivotal role in refining models for optimal performance. These algorithms, ranging from classic gradient descent to advanced techniques like stochastic gradient descent (S…GD), Adam, and RMSprop, are fundamental in minimizing the error function and enhancing model accuracy. Each algorithm offers unique advantages: SGD efficiently handles large datasets by updating parameters iteratively, while Adam adapts learning rates dynamically based on gradient variance.Theoretical understanding of optimization algorithms involves comprehending concepts like convexity, convergence criteria, and the impact of learning rate adjustments. Practically, implementing these algorithms requires tuning hyperparameters and balancing computational efficiency with model effectiveness. Moreover, recent advancements such as meta-heuristic algorithms (e.g., genetic algorithms) expand optimization capabilities for complex, non-convex problems.Mastering optimization algorithms equips practitioners with the tools to improve model robustness and scalability across diverse applications, ensuring machine learning systems perform optimally in real-world scenarios.tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 340 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 31,23
Envío por EUR 62,88Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the realm of machine learning, optimization algorithms play a pivotal role in refining models for optimal performance. These algorithms, ranging from classic gradient descent to advanced techniques like stochastic gradient descent (SG…D), Adam, and RMSprop, are fundamental in minimizing the error function and enhancing model accuracy. Each algorithm offers unique advantages: SGD efficiently handles large datasets by updating parameters iteratively, while Adam adapts learning rates dynamically based on gradient variance.Theoretical understanding of optimization algorithms involves comprehending concepts like convexity, convergence criteria, and the impact of learning rate adjustments. Practically, implementing these algorithms requires tuning hyperparameters and balancing computational efficiency with model effectiveness. Moreover, recent advancements such as meta-heuristic algorithms (e.g., genetic algorithms) expand optimization capabilities for complex, non-convex problems.Mastering optimization algorithms equips practitioners with the tools to improve model robustness and scalability across diverse applications, ensuring machine learning systems perform optimally in real-world scenarios.
