Classification
A two-task deep learning project for the ED110135 Mobile Computer Vision lab course at TUM. The headline task trains a lightweight MobileNet to classify overhead satellite tiles as
plane/notplane, a problem directly relevant to remote sensing, search-and-rescue, and edge-deployed earth observation. A parallel tabular task (Ames housing “expensive vs. not”) was built with vanilla gradient descent and a sigmoid classifier to contrast image-based deep learning with classical binary classification. Built in Python / TensorFlow / Keras, evaluated through systematic optimizer × epoch ablations rather than single accuracy numbers.