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

Exploring Optimization Algorithms in Machine Learning: From Theory to Practice

Szerző: Kinky
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: tredition
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
7 366 Ft
Optimization algorithms in machine learning bridge theoretical foundations with practical applicatio...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2024
oldal
340
EAN
9783384275837
ISBN
3384275837
Enbook ID
46199574
Kiadó
Súly
576
Méretek
155 x 234 x 24

Teljes leírás

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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