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Training costs are therefore higher, and increased training time means less operational performance time.
The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time.
However, increased training time associated with training all the component networks, as well as generating their outputs for student training, could be an obstacle.
When comparing padded vs. non-padded convolutional layers, we found little difference in classification performance; however, using padding to enable creation of deeper networks increased training time by 2.72×, and using padding while doubling the number of convolutional layers increased training time by 3.26×.
Increased training time and IT support also aided in the successful implementation.
Deep learning programs excel at learning from unstructured data at the cost of increased training time and computational power.
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As an added benefit, this gain in performance is not obtained at the expense of highly increased training times due to deployment of GPUs.
The aim was to accelerate the development of IT expertise without increasing training time.
As neurons increase, training time increases dramatically, but the company's neuromorphic hardware scales well in time, but take up a lot of space on a chip.
Also, while padding allows deep networks to be constructed with a variety of architectures, it does increase training time.
One drawback of ANFIS is that an increase in the number of network inputs for building an ANFIS model will largely increase training time and computing resources.
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