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