A) TRUE B) FALSE
A) Pattern recognition B) Classification C) Clustering D) All of these
A) What-if question B) IF-The-Else Analysis Questions C) For Loop questions
A) Robustness B) Adaptive Learning C) Self Organization D) Fault tolerance
A) Supervised Learning B) What-If Analysis C) Adaptive Learning D) Self Organization
A) weights B) axons C) Soma D) nodes or neurons
A) activation function B) neurons C) weights D) bias
A) FALSE B) TRUE
A) None of these B) Weight C) activation or activity level of neuron D) Bias
A) one B) none C) any number of D) multiple
A) Multi layered perceptron B) Recurrent neural network C) Self organizing maps D) Perceptrons
A) Active learning B) Unsupervised learning C) Reinforcement learning D) Supervised learning
A) No specific Inputs are given B) Specific output values are given C) specific output values are not given D) Both inputs and outputs are given
A) Nonlinear Functions B) Linear Functions C) Exponential Functions D) Discrete Functions
A) Recurrent neural networks B) Feedforward neural networks
A) Feedforward neural networks B) Recurrent neural networks
A) Deterministic B) Dynamic C) Static
A) human have sense organs B) human have more IQ & intellect C) human perceive everything as a pattern while machine perceive it merely as data D) human have emotions
A) brain B) axon C) neuron D) nucleus
A) the system recalls previous reference inputs & respective ideal outputs B) the system learns from its past mistakes C) the strength of neural connection get modified accordingly |