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