Mengejar Kinerja Maksimal: Teknik Pengoptimalan Terkini dalam Pembelajaran Mesin

Abstract

In the era of data revolution and artificial intelligence, machine learning model optimization has become one of the most dynamic and crucial research areas. This article reviews the latest techniques in machine learning model optimization with a focus on pursuing maximum performance. We discuss various methods applied to improve model accuracy and efficiency, ranging from hyperparameter tuning techniques, advanced optimization algorithms such as Bayesian optimization, to innovative approaches such as meta-learning and transfer learning. These optimization techniques not only aim to improve model performance but also to overcome challenges related to big data, model complexity, and computational limitations. We investigate how these methods can be integrated in machine learning pipelines to achieve better results with more efficient resources. Through a review of recent literature and case studies of applications in various domains, this article provides in-depth insights into the trends and developments in model optimization, as well as practical recommendations for researchers and practitioners in pursuing maximum performance from their machine learning systems. A better understanding of these cutting-edge techniques is expected to facilitate the achievement of better and more innovative results in future machine learning applications.