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