Techniques for Efficient Training of Large-Scale Deep Learning Models

Abstract

The training of large-scale machine learning models has become a cornerstone of modern artificial intelligence (AI) research and applications. However, the computational demands and resource requirements associated with training such models are substantial, often leading to increased costs and longer training times. This paper reviews various strategies and techniques that have been developed to enhance the efficiency of training large-scale models. We focus on innovations in distributed computing, optimization algorithms, and hardware accelerators, and discuss their implications for scalability and performance.