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