A) TRUE B) FALSE
A) Classification B) All of these C) Pattern recognition D) Clustering
A) What-if question B) For Loop questions C) IF-The-Else Analysis Questions
A) Fault tolerance B) Adaptive Learning C) Robustness D) Self Organization
A) What-If Analysis B) Self Organization C) Supervised Learning D) Adaptive Learning
A) nodes or neurons B) Soma C) weights D) axons
A) neurons B) bias C) activation function D) weights
A) TRUE B) FALSE
A) Weight B) None of these C) activation or activity level of neuron D) Bias
A) none B) multiple C) one D) any number of
A) Perceptrons B) Self organizing maps C) Recurrent neural network D) Multi layered perceptron
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) Both inputs and outputs are given D) No specific Inputs are given
A) Discrete Functions B) Linear Functions C) Exponential Functions D) Nonlinear 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 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) 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 |