1 Introduction  
2 Deep Learning Model Complexity  
2.1 What is Deep Learning Model Complexity?  
2.2 Important Factors of Deep Learning Model Complexity  
2.3 Existing Studies on Deep Learning Model Complexity: An Overview  
3 Expressive Capacity of Deep Learning Models  
3.1 Depth Efficiency  
3.2 Width Efficiency  
3.3 Expressible Functional Space  
3.4 VC Dimension and Rademacher Complexity  
4 Effective Complexity of Deep Learning Models  
4.1 General Measures of Effective Complexity  
4.2 High-Capacity Low-Reality Phenomenon  
4.3 Discussion  
5 Application Examples of Deep Learning Model Complexity  
5.1 Model Complexity in Understanding Model Generalization Capability  
5.2 Model Complexity in Optimization  
5.3 Model Complexity in Model Selection and Design  
6 Conclusions and Future Directions