| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === As technology advances, deep learning has changed the way many industries produce, such as detecting defects, identifying objects, and so on. The core network model is the core of the algorithm and the essence of the training after big data. However, for most operators, how to train a usable model from scratch is a major difficulty in introducing artificial intelligence on the production line. How to quickly and easily complete a usable deep learning model becomes an issue that most non-employed workers want to know.
Usually, training a highly accurate deep learning model requires a complex network architecture in addition to a large amount of data, and can be completed after numerous fine-tuning. The acquisition of data is relatively easy for the production line operators, and the network architecture needs to take time to understand the details. It is not completed in a moment and a half, and the threshold for entry is relatively improved, which is not conducive to industrial upgrading of various factories.
This study combines the deep learning model suite Keras with the web language of the client and server side to provide a web-based artificial intelligence system that can quickly train deep learning models. The system allows users to set parameters and upload training data through the graphical interface, so that users in non-employed fields can quickly train the required models without spending too much time on the details of deep learning.
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