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