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Sinopsis
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.
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Optimization Algorithms for Machine Learning: Theory and Practice (Paperback or Softback)
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Tredition Gmbh 7/8/2024, 2024
ISBN 10: 3384283376
ISBN 13: 9783384283375
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Paperback or Softback. Condición: New. Optimization Algorithms for Machine Learning: Theory and Practice 1.1. Book. Nº de ref. del artículo: BBS-9783384283375
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Optimization Algorithms for Machine Learning: Theory and Practice
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Optimization Algorithms for Machine Learning: Theory and Practice
Publicado por
Tredition Gmbh, 2024
ISBN 10: 3384283376
ISBN 13: 9783384283375
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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
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Optimization Algorithms for Machine Learning: Theory and Practice
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
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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
Cantidad disponible: 1 disponibles
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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
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Optimization Algorithms for Machine Learning: Theory and Practice
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Optimization Algorithms for Machine Learning: Theory and Practice
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. 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.tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 340 pp. Englisch. Nº de ref. del artículo: 9783384283375
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Optimization Algorithms for Machine Learning (Paperback)
Librería: CitiRetail, Stevenage, Reino Unido
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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
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Optimization Algorithms for Machine Learning: Theory and Practice
Librería: preigu, Osnabrück, Alemania
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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. Nº de ref. del artículo: 129600017
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