DeepDebugger is an interactive and visualized time-traveling approach for debugging deep classifiers. Specifically, DeepDebuuger records the training process of deep classifiers and projects the high-dimensional classification landscape into a two-dimensional space. Users can view the training dynamics of the high-dimensional classification landscape in the low-dimensional space, as a visualized animation. DeepDebuuger contributes to recommending user-interested samples in a human-in-the-loop manner. Given a debugging task requiring sample inspection, DeepDebuuger recommends what samples need to be (re)labelled or what samples are suspicious. Users can provide feedback on the recommendation, and DeepDebuuger can interactively recommend new samples, guiding the users to pinpoint the root cause of model bugs like misprediction.