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