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- PublisherTredition Gmbh
- Publication date2024
- ISBN 10 3384283376
- ISBN 13 9783384283375
- BindingPaperback
- LanguageEnglish
- Number of pages340
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Optimization Algorithms for Machine Learning: Theory and Practice
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Optimization Algorithms for Machine Learning: Theory and Practice (Paperback or Softback)
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ISBN 10: 3384283376
ISBN 13: 9783384283375
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Paperback or Softback. Condition: New. Optimization Algorithms for Machine Learning: Theory and Practice 1.1. Book. Seller Inventory # BBS-9783384283375
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Optimization Algorithms for Machine Learning: Theory and Practice
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Optimization Algorithms for Machine Learning (Paperback)
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Paperback. Condition: 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 multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 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
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Optimization Algorithms for Machine Learning: Theory and Practice
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Taschenbuch. Condition: 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. Seller Inventory # 9783384283375
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Optimization Algorithms for Machine Learning: Theory and Practice
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Taschenbuch. Condition: 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. Seller Inventory # 9783384283375
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Optimization Algorithms for Machine Learning: Theory and Practice
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Taschenbuch. Condition: Neu. Optimization Algorithms for Machine Learning: Theory and Practice | Prashad | Taschenbuch | Paperback | Englisch | 2024 | tredition | EAN 9783384283375 | Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 129600017
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