Machine Learning, Deep Learning, Research, Programming, Mathematics.
engineering approaches can highlight “objects of interest” in input data. Needs a lot of data: deep learning doesn’t really work on 10–100 samples. Unsupervised learning: most of the applications now require labeling of the training data Zero-shot reuse: this is a really important issue today: a