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