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