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