Name
Abstract | ||
|---|---|---|
Deep learning has become a hot topic in recent years. There are many teaching frameworks that ease the education process for deep learning. However, most current teaching examples either require a lot of training time or do not have interaction with users. Usually, the testing accuracy is the only evaluation criterion. However, it does not mean too much for a novice. We provide a framework that can teach the deep learning concepts with limited time or computational resource constraints. It offers students quick feedback, i.e., the trained model can be easily tested on this framework. Students can increase training data, design a deep learning model, or modify input planes to learn more advanced topics. The framework provides a graphical user interface, which connects to the trained model directly. It is a well-designed deep learning competition framework. This framework has been applied in the “artificial intelligence” undergraduate course for three semesters. The student feedback was quite positive. Furthermore, based on this framework, deep learning Othello competitions were held in TAAI and TCGA international computer game tournaments in 2017. We hope that this framework can encourage more people to be engaged in the research of deep learning and board games. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TG.2019.2931153 | IEEE Transactions on Games |
Keywords | DocType | Volume |
Games,Deep learning,Training,Testing,Computational modeling,Neural networks | Journal | 11 |
Issue | ISSN | Citations |
3 | 2475-1502 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Nung Lin | 1 | 0 | 2.03 |
| Jr-Chang Chen | 2 | 42 | 15.19 |
| Shi-jim Yen | 3 | 134 | 27.99 |