2024, Vol. 6, Special Issue 6, Part C

Author(s): Palak Kalsi

Abstract: In the realm of learning models, there exists an ongoing opportunity for advancement. Initially, within machine learning, the notion involved applying task-specific algorithms to machines, enabling them to execute limited tasks. Subsequently, the emergence of deep learning empowered machines to learn from a diverse range of available data and analyze it using neural networks. However, this approach faces limitations, particularly regarding the availability of diverse data and resources. In scenarios where data variety and resources are lacking, results may vary. The introduction of meta-learning revolutionizes the learning process by examining experiences from multiple learning tasks and utilizing them to enhance future learning performances. Meta-learning, defined as the ability to learn how to learn, brings benefits by improving data and computational efficiency. In the global success of AI, each state has a significant role to play: machine learning improves engineering features, while deep learning enhances feature representation. Meta-learning within neural networks aims to replace manually designed algorithms with learned learning algorithms. This paper aims to explore the objectives and features of meta-learning and how its attributes address the limitations of traditional learning models, including those of deep learning and machine learning. It delves into issues such as computational challenges, efficiency requirements, and the significant fields where meta-learning finds application. Additionally, it discusses the potential applications of meta-learning across various domains such as natural language processing (NLP), speech recognition, design, and beyond. The primary emphasis is on how meta-learning contributes to the evolution of NLP and enhances the efficiency of NLP models.

DOI: 10.33545/27068919.2024.v6.i6c.1241

Pages: 194-197 | Views: 1223 | Downloads: 135

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