Artículos relacionados a Optimization Algorithms for Machine Learning: Theory...
- Editorialtredition
- Año de publicación2024
- ISBN 10 3384283376
- ISBN 13 9783384283375
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas340
Comprar nuevo
Ver este artículo
EUR 19,81
EUR 2,36 gastos de envío en Estados Unidos de America
Destinos, gastos y plazos de envíoResultados de la búsqueda para Optimization Algorithms for Machine Learning: Theory...
Imagen de archivo
Optimization Algorithms for Machine Learning: Theory and Practice
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 48005188-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Optimization Algorithms for Machine Learning: Theory and Practice (Paperback or Softback)
Publicado por
Tredition Gmbh 7/8/2024, 2024
ISBN 10: 3384283376
ISBN 13: 9783384283375
Nuevo
Paperback or Softback
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback or Softback. Condición: New. Optimization Algorithms for Machine Learning: Theory and Practice 1.1. Book. Nº de ref. del artículo: BBS-9783384283375
Cantidad disponible: 5 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning: Theory and Practice
Publicado por
Tredition Gmbh, 2024
ISBN 10: 3384283376
ISBN 13: 9783384283375
Antiguo o usado
Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 48005188
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning (Paperback)
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. 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 (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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783384283375
Cantidad disponible: 1 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning: Theory and Practice
Publicado por
Tredition Gmbh, 2024
ISBN 10: 3384283376
ISBN 13: 9783384283375
Antiguo o usado
Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 48005188
Gastos de envío:
EUR 17,84
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning: Theory and Practice
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 48005188-n
Gastos de envío:
EUR 17,84
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning: Theory and Practice
Impresión bajo demandaLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
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 descent (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. Nº de ref. del artículo: 9783384283375
Gastos de envío:
EUR 23,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen del vendedor
Optimization Algorithms for Machine Learning: Theory and Practice
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
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 (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. Nº de ref. del artículo: 9783384283375
Gastos de envío:
EUR 30,87
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen de archivo
Optimization Algorithms for Machine Learning (Paperback)
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. 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 (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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9783384283375
Gastos de envío:
EUR 44,00
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen del vendedor
Optimization Algorithms for Machine Learning: Theory and Practice
Librería: preigu, Osnabrück, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Optimization Algorithms for Machine Learning: Theory and Practice | Prashad | Taschenbuch | Paperback | Englisch | 2024 | tredition | EAN 9783384283375 | Verantwortliche Person für die EU: tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu. Nº de ref. del artículo: 129600017
Gastos de envío:
EUR 70,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 5 disponibles