A) TRUE B) FALSE
A) All of these B) Clustering C) Classification D) Pattern recognition
A) What-if question B) For Loop questions C) IF-The-Else Analysis Questions
A) Self Organization B) Fault tolerance C) Robustness D) Adaptive Learning
A) Adaptive Learning B) Self Organization C) Supervised Learning D) What-If Analysis
A) weights B) nodes or neurons C) axons D) Soma
A) neurons B) bias C) weights D) activation function
A) FALSE B) TRUE
A) None of these B) Weight C) Bias D) activation or activity level of neuron
A) none B) any number of C) multiple D) one
A) Self organizing maps B) Recurrent neural network C) Perceptrons D) Multi layered perceptron
A) Active learning B) Supervised learning C) Unsupervised learning D) Reinforcement 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) Linear Functions B) Discrete Functions C) Nonlinear Functions D) Exponential Functions
A) Recurrent neural networks B) Feedforward neural networks
A) Recurrent neural networks B) Feedforward neural networks
A) Dynamic B) Static C) Deterministic
A) human have more IQ & intellect B) human have sense organs C) human have emotions D) human perceive everything as a pattern while machine perceive it merely as data
A) nucleus B) brain C) neuron D) axon
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 |