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