Exploring Optimization Algorithms in Machine Learning: From Theory to Practice

Name: Exploring Optimization Algorithms in Machine Learning: From Theory to Practice
Brand: tredition
SKU: 9783384275837
Price: 18.6 EUR
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Autor: Kinky

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Väzba: Brožovaná

Vydavateľ: tredition

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Optimization algorithms in machine learning bridge theoretical foundations with practical applicatio...

Informácie o knihe

Autor

Kinky

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2024

Stránok

340

EAN

9783384275837

ISBN

3384275837

Enbook ID

46199574

Vydavateľ

tredition

Hmotnosť

576

Rozmery

155 x 234 x 24

Kompletný popis

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.

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Exploring Optimization Algorithms in Machine Learning: From Theory to Practice

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