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