Optimization Algorithms for Machine Learning: Theory and Practice - Prashad
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Professor Rahul Prasad is a renowned expert in the field of concrete science and materials, with a particular focus on Alkali-Aggregate Reaction (AAR) mitigation. He brings a wealth of experience to the table, having conducted extensive research, published groundbreaking studies, and provided crucial guidance to the construction industry worldwide. Professor Prasad's dedication to combating AAR, also known as concrete cancer, is evident in his authorship of "Combating Concrete Cancer: A Global Guide to Alkali-Aggregate Reaction Mitigation." This comprehensive text serves as a valuable resource for engineers, builders, and material scientists working to ensure the durability and longevity of concrete infrastructure across the globe. Professor Prasad's contributions extend beyond academia. He actively collaborates with industry professionals, policymakers, and researchers to develop and implement effective AAR mitigation strategies. His commitment to knowledge sharing and capacity building empowers stakeholders around the world to construct safer, more sustainable concrete structures.