Name: Exploring Optimization Algorithms in Machine Learning: From Theory to Practice (en Inglés)
Price: 102.46 PEN
Availability: InStock
Author: Kinky
ISBN: 9783384275837

portada Exploring Optimization Algorithms in Machine Learning: From Theory to Practice (en Inglés)

Exploring Optimization Algorithms in Machine Learning: From Theory to Practice (en Inglés)

Formato

Libro Físico

Editorial

Tredition Gmbh

Autor

Kinky

Idioma

Inglés

N° páginas

340

Encuadernación

Tapa Blanda

ISBN13

9783384275837

Exploring Optimization Algorithms in Machine Learning: From Theory to Practice (en Inglés)

Kinky (Autor) · Tredition Gmbh · Tapa Blanda

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Reseña del libro "Exploring Optimization Algorithms in Machine Learning: From Theory to Practice (en Inglés)"

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.