Deep learning practitioners are often interested in improving their model accuracy rather than the interpretability of their models. As a result, deep learning applications are inherently complex in their structures. They also need to continuously evolve in terms of code changes and model updates. Given these confounding factors, there is a great chance of violating the recommended programming practices by the developers in their deep learning applications. In particular, the code quality might be negatively affected due to their drive for the higher model performance. Unfortunately, the code quality of deep learning applications has rarely been studied to date. In this paper, we conduct an empirical study using 118 open-source software systems from GitHub where we contrast between deep learning-based and traditional systems in terms of their code quality. We have several major findings. First, deep learning applications smell like the traditional ones. However, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, the DL code involves more complex or longer expressions than the traditional code does. Second, code smells are found increasing across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the deep learning code, which confirms our conjecture on the degraded code quality in deep learning applications.