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We are witnessing the third rise of deep learning. The first two waves — 1950s–1960s and 1980s–1990s — generated considerable excitement but slowly ran out of steam, since these neural networks neither achieved their promised performance gains nor aided our understanding of biological vision systems. The third wave — 2000s–present — is different because deep learning has blown past its competition on a plethora of benchmarks and real world applications. While most of the basic ideas of deep learning were already developed during the second wave, their power could not be unleashed until large datasets and powerful computers (GPUs) became available. The rise and fall of deep learning reflec... Full story

11 February