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