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