Articles liés à Exploring Optimization Algorithms in Machine Learning:...
Synopsis
Optimization algorithms in machine learning bridge theoretical foundations with practical applications, crucial for refining model performance. Techniques like gradient descent, stochastic gradient descent (SGD), and advanced methods such as Adam and RMSprop optimize model parameters to minimize error and enhance accuracy. Theoretical understanding encompasses concepts like convexity, convergence criteria, and adaptive learning rates, essential for algorithm selection based on dataset characteristics. In practice, implementing these algorithms involves tuning hyperparameters and assessing trade-offs between computational efficiency and model effectiveness across diverse datasets. Recent innovations, including meta-heuristic algorithms like genetic algorithms, further expand optimization capabilities for complex, non-linear problems. Mastering optimization algorithms empowers practitioners to navigate challenges in model training and deployment effectively, ensuring robust performance in real-world applications. This comprehensive understanding supports innovation in machine learning, driving advancements in various fields from healthcare to finance and beyond.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 31,26
EUR 10,99 expédition depuis Allemagne vers France
Destinations, frais et délaisRésultats de recherche pour Exploring Optimization Algorithms in Machine Learning:...
Image fournie par le vendeur
Exploring Optimization Algorithms in Machine Learning: From Theory to Practice
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimization algorithms in machine learning bridge theoretical foundations with practical applications, crucial for refining model performance. Techniques like gradient descent, stochastic gradient descent (SGD), and advanced methods such as Adam and RMSprop optimize model parameters to minimize error and enhance accuracy. Theoretical understanding encompasses concepts like convexity, convergence criteria, and adaptive learning rates, essential for algorithm selection based on dataset characteristics.In practice, implementing these algorithms involves tuning hyperparameters and assessing trade-offs between computational efficiency and model effectiveness across diverse datasets. Recent innovations, including meta-heuristic algorithms like genetic algorithms, further expand optimization capabilities for complex, non-linear problems.Mastering optimization algorithms empowers practitioners to navigate challenges in model training and deployment effectively, ensuring robust performance in real-world applications. This comprehensive understanding supports innovation in machine learning, driving advancements in various fields from healthcare to finance and beyond. N° de réf. du vendeur 9783384275837
Quantité disponible : 1 disponible(s)
Image d'archives
Exploring Optimization Algorithms in Machine Learning: From Theory to Practice
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Optimization algorithms in machine learning bridge theoretical foundations with practical applications, crucial for refining model performance. Techniques like gradient descent, stochastic gradient descent (SGD), and advanced methods such as Adam and RMSprop optimize model parameters to minimize error and enhance accuracy. Theoretical understanding encompasses concepts like convexity, convergence criteria, and adaptive learning rates, essential for algorithm selection based on dataset characteristics.In practice, implementing these algorithms involves tuning hyperparameters and assessing trade-offs between computational efficiency and model effectiveness across diverse datasets. Recent innovations, including meta-heuristic algorithms like genetic algorithms, further expand optimization capabilities for complex, non-linear problems.Mastering optimization algorithms empowers practitioners to navigate challenges in model training and deployment effectively, ensuring robust performance in real-world applications. This comprehensive understanding supports innovation in machine learning, driving advancements in various fields from healthcare to finance and beyond. 340 pp. Englisch. N° de réf. du vendeur 9783384275837
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Exploring Optimization Algorithms in Machine Learning: From Theory to Practice
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Optimization algorithms in machine learning bridge theoretical foundations with practical applications, crucial for refining model performance. Techniques like gradient descent, stochastic gradient descent (SGD), and advanced methods such as Adam and RMSprop optimize model parameters to minimize error and enhance accuracy. Theoretical understanding encompasses concepts like convexity, convergence criteria, and adaptive learning rates, essential for algorithm selection based on dataset characteristics.In practice, implementing these algorithms involves tuning hyperparameters and assessing trade-offs between computational efficiency and model effectiveness across diverse datasets. Recent innovations, including meta-heuristic algorithms like genetic algorithms, further expand optimization capabilities for complex, non-linear problems.Mastering optimization algorithms empowers practitioners to navigate challenges in model training and deployment effectively, ensuring robust performance in real-world applications. This comprehensive understanding supports innovation in machine learning, driving advancements in various fields from healthcare to finance and beyond.tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 340 pp. Englisch. N° de réf. du vendeur 9783384275837
Quantité disponible : 2 disponible(s)
Image d'archives
Exploring Optimization Algorithms in Machine Learning (Paperback)
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Optimization algorithms in machine learning bridge theoretical foundations with practical applications, crucial for refining model performance. Techniques like gradient descent, stochastic gradient descent (SGD), and advanced methods such as Adam and RMSprop optimize model parameters to minimize error and enhance accuracy. Theoretical understanding encompasses concepts like convexity, convergence criteria, and adaptive learning rates, essential for algorithm selection based on dataset characteristics. In practice, implementing these algorithms involves tuning hyperparameters and assessing trade-offs between computational efficiency and model effectiveness across diverse datasets. Recent innovations, including meta-heuristic algorithms like genetic algorithms, further expand optimization capabilities for complex, non-linear problems. Mastering optimization algorithms empowers practitioners to navigate challenges in model training and deployment effectively, ensuring robust performance in real-world applications. This comprehensive understanding supports innovation in machine learning, driving advancements in various fields from healthcare to finance and beyond. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783384275837
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Exploring Optimization Algorithms in Machine Learning: From Theory to Practice
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Exploring Optimization Algorithms in Machine Learning: From Theory to Practice | Kinky | Taschenbuch | Englisch | 2024 | Mia Graf | EAN 9783384275837 | Verantwortliche Person für die EU: tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu. N° de réf. du vendeur 129556350
Quantité disponible : 5 disponible(s)