Gradient descent and its variants are de facto standard algorithms for training machine learning models. As gradient descent is sensitive to its hyperparame-ters, we need to tune the hyperparameters carefully using a grid search.

However, they cause another problem: space waste for personal tasks.

Unqualified images are removed as described in Appendix A.3. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to

Atmospheric turbulence, caused by random fluctuations in the atmosphere's refractive index, introduces complex spatio-temporal distortions in imagery captured

As a motivating example, consider deploying a robot agent at scale in a varietyofhomeenvironments. Therobotshouldgeneralize byperforming robustlynotonlyintest homes, butinanyenduser'shome.

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