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