In this chapter, we talked about some important tips and tricks that can be used for preparing your data for deep learning, optimization/training, and leveraging existing pre-trained models. In practice, you may face complicated scenarios that may not be solved by these general/standard techniques directly. However, a rule of thumb is that you should always start with trying to understand your data and problem better, and at the same time dive into the learning process (for example, utilizing some visualization tool) to understand how information has been processed and learned by the network. Such understanding will be extremely valuable to you when debugging the model and improving the results.
In the next chapter, we will be going through various trends in deep learning.
