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Also, while padding allows deep networks to be constructed with a variety of architectures, it does increase 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.
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.
Alternative training methods, such as on-the-job mentoring and course prerequisites of online learning, could help increase training time available for clinical supervision.
While some older adults may be unfamiliar with computer games, we anticipate their inclusion will increase training time as recent studies show promising results in this regard, even among the very old [ 32, 33].
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The aim was to accelerate the development of IT expertise without increasing 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.
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.
Though padding increases training time, it allowed us to build a network with six convolutional layers resulting in a 2.2 2.4% increase in classification accuracy.
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.
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