Two things often mentioned with deep learning are 'data' and 'compute resources.' You need a lot of both when developing, training, and testing deep learning models
"When developers don't have a lot of training samples or access to very powerful servers, they use transfer learning to finetune a pre-trained deep learning model for a new task." writes
Ben Dickson in TechTalks.
"At this year's ICML conference, scientists at IBM Research and Taiwan's National Tsing Hua University Research introduced 'black-box adversarial reprogramming' (BAR), an alternative repurposing technique that turns a supposed weakness of deep neural networks into a strength..."
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