optimization algorithms machine learning di prashad (9 risultati)

Condizione: New.

Paperback or Softback. Condizione: New. Optimization Algorithms for Machine Learning: Theory and Practice. Book.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Taschenbuch. Condizione: 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.

Taschenbuch. Condizione: Neu. Optimization Algorithms for Machine Learning: Theory and Practice | Prashad | Taschenbuch | Englisch | 2024 | tredition | EAN 9783384283375 | Verantwortliche Person für die EU: tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: 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.

Taschenbuch. Condizione: 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.