A) TRUE B) FALSE
A) Clustering B) All of these C) Classification D) Pattern recognition
A) IF-The-Else Analysis Questions B) For Loop questions C) What-if question
A) Self Organization B) Adaptive Learning C) Robustness D) Fault tolerance
A) Adaptive Learning B) What-If Analysis C) Self Organization D) Supervised Learning
A) axons B) weights C) Soma D) nodes or neurons
A) bias B) activation function C) neurons D) weights
A) TRUE B) FALSE
A) Weight B) None of these C) Bias D) activation or activity level of neuron
A) one B) none C) multiple D) any number of
A) Perceptrons B) Recurrent neural network C) Multi layered perceptron D) Self organizing maps
A) Reinforcement learning B) Active learning C) Unsupervised learning D) Supervised 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) Nonlinear Functions B) Discrete Functions C) Exponential Functions D) Linear Functions
A) Recurrent neural networks B) Feedforward neural networks
A) Recurrent neural networks B) Feedforward neural networks
A) Static B) Dynamic C) Deterministic
A) human have emotions B) human perceive everything as a pattern while machine perceive it merely as data C) human have sense organs D) human have more IQ & intellect
A) neuron B) nucleus C) axon D) brain
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 |