Optimization Algorithms for Machine Learning: Theory and Practice

Language: English

Published by Tredition, 2024

3384283376 / 9783384283375

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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.…

Seller Inventory # 9783384283375

Title
Optimization Algorithms for Machine Learning: Theory and Practice
Author
Prashad
Publisher
Tredition
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3384283376
ISBN 13
9783384283375
Item weight
576 grams
Dimensions
234x155x24 mm

AHA-BUCH GmbH

Einbeck, Germany

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