Comparative Analysis of Machine Learning and Deep Learning in Practical Scenarios

Abstract

With the emergence of ChatGPT, machine learning (ML) and deep learning (DL) technology have gradually attracted people's attention, and these two technologies have already penetrated People's Daily life unconsciously. However, the application of these two technologies in various industries is still in the development stage, and choosing an inappropriate technology in different scenarios will lead to problems such as a waste of resources. The purpose of this article is to introduce the basic concepts, principles, and common algorithms of these two technologies, and by comparing the results of different technologies in the same scene, this article will compare and analyze the advantages and disadvantages of ML and DL in feature generation, model complexity, data scale, etc. Finally, the different application scenarios of the two technologies are obtained. ML is better for situations where there is less data and the problem is relatively simple, while DL performs better for tasks that deal with large and complex data and require hierarchical feature representation. This article hopes to inspire problem-solving in this subject area.