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