MACHINE LEARNING

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APPLICATION OF SUPERVISED LEARNING

DEEP LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
What are general limitations of back propagation rule? Pick the right choice from the given options i) local minima problem ii) slow convergence iii) scaling
A
only i
B
both ii and i
C
both ii and iii
D
i, ii and iii
Explanation: 

Detailed explanation-1: -What are general limitations of back propagation rule? a) local minima problem b) slow convergence c) scaling d) all of the mentioned Answer: d Explanation: These all are limitations of backpropagation algorithm in general.

Detailed explanation-2: -Because each expert is only utilized for a few instances of inputs, back-propagation is slow and unreliable. And when new circumstances arise, the Mixture of Experts cannot adapt its parsing quickly. If a circumstance requires a new kind of expertise, existing Mixtures of Experts cannot add that specialization.

Detailed explanation-3: -It prefers a matrix-based approach over a mini-batch approach. Data mining is sensitive to noise and irregularities. Performance is highly dependent on input data. Training is time-and resource-intensive.

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