Comparative Study on the Effectiveness of Automated Machine Learning in Image Recognition Applications

• Main Authors: Hui-Cin Chan, 詹惠琹
• Other Authors: Shuo-Yan Chou
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/mcr6en

碩士 === 國立臺灣科技大學 === 工業管理系 === 107 === In the past few years, the success of machine learning in a variety of applications has led to a rapid increase in the demand for machine learning systems. A typical machine learning model includes the following processes: from ingesting data to pre-processing, optimization, and predicting results, each step is manually controlled and executed. The current success of machine learning must rely on experienced experts to select the appropriate features, models, optimization, and evaluation of important steps. However, in order for machine learning to be used in large numbers in the future, it is necessary to find a universal and easy-to-use method. The research field that automates machine learning is called AutoML. The full name is Automated Machine Learning, which is machine learning and since 2014. One of the hottest areas of deep learning. The topic of automated machine learning is becoming more and more important. Google AI uses AutoML to find a better alternative architecture for convolutional networks to classify images in autonomous vehicles. The marketing department’s reporting rate has increased by 30% and accuracy has increased by 8%, which is impressive. At present, there are many tools and platforms on the market that claim to have AutoML, but most of them only provide hyper-parameter tuning, but there is no data clearing, data exploration or data transposition function. Some emphasize the function of autoML, and some emphasize it. User experience (UX) or a service that provides more information visualization. Invariably, they all regard the intelligent automation model as the most important task. This thesis aims to understand the current framework of autoML concepts and applications, and to identify images and understand the operation methods and performance through mnist handwriting.