Kniha Exploring Optimization Algorithms in Machine Learning: From Theory to Practice Kinky

Exploring Optimization Algorithms in Machine Learning: From Theory to Practice

Autor: Kinky
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: tredition
Dostupnost: Skladem u dodavatele
Odesíláme za 9-15 dnů
450
Optimization algorithms in machine learning bridge theoretical foundations with practical applicatio...

Informace o knize

Autor
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2024
Stránek
340
EAN
9783384275837
ISBN
3384275837
Enbook ID
46199574
Vydavatel
Hmotnost
576
Rozměry
155 x 234 x 24

Kompletní 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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