Growing pains for deep learning

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Albumentations: fast and flexible image augmentations (2018, background): “inations of a wide range of various transformations to the task in hand. While the largest gains in efﬁciency of deep neural networks have come from computation with graphical processing units (GPUs) [9], which excel at the matrix and vector operations central to deep learning, data pre-processing a”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “But specialized hardware promises improvements in deep learning at reduced time, energy, and memory [382].”