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