Exploring Optimization Algorithms in Machine Learning (Paperback)

Kinky

ISBN 10: 3384275837 / ISBN 13: 9783384275837

Verlag: Tredition Gmbh, 2024

Beschreibung

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.

Bestandsnummer des Verkäufers 9783384275837

Bibliografische Details

• Titel: Exploring Optimization Algorithms in Machine Learning (Paperback)
• Verlag: Tredition Gmbh
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 3384275837
• ISBN-13: 9783384275837
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.