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