Deep Learning Optimization Conception: Less Data, Less Time, More Performance

Abstract

Although Deep Learning has not made a breakthrough in terms of artificial intelligence core technology, it achieves the best worldwide performance across areas such as computer vision and natural language processing. However, it depends on large-scale datasets and enormous computational resources. This paper tackles a major issue: Can we train more efficient deep learning models with less data in less time? We look at numerous strategies designed to reduce the burden of training, without letting the quality deteriorate. From transfer learning and few-shot learning to lightweight architectures, synthetic datasets produced artificially, as well as dispersed training, we contemplate how to make advanced AI subsystems fit for running under scarce resources. The aim is to lay down a future for deep learning that is more sustainable and all-embracing. This research focuses on the important issue of streamlining deep learning models with balancing model performance against data collection and computations. We look into other approaches such as transfer learning, couple with fewshot learning, data augmentation, architecture optimization, and parallelization. We explain processes with their benefits as well as their setbacks. Our research shows that training a model more efficiently improves the overall training process, making it cheaper and greener. A change like this would help more people use sophisticated AI systems even when limited by constrained resources. This broadens the real-world application of AI technology and further stimulates innovation in the area.