A) TRUE B) FALSE
A) All of these B) Classification C) Clustering D) Pattern recognition
A) IF-The-Else Analysis Questions B) For Loop questions C) What-if question
A) Fault tolerance B) Self Organization C) Adaptive Learning D) Robustness
A) Self Organization B) Supervised Learning C) What-If Analysis D) Adaptive Learning
A) weights B) Soma C) nodes or neurons D) axons
A) activation function B) weights C) bias D) neurons
A) TRUE B) FALSE
A) activation or activity level of neuron B) None of these C) Bias D) Weight
A) none B) one C) multiple D) any number of
A) Self organizing maps B) Perceptrons C) Multi layered perceptron D) Recurrent neural network
A) Reinforcement learning B) Active learning C) Unsupervised learning D) Supervised learning
A) Both inputs and outputs are given B) specific output values are not given C) Specific output values 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) Recurrent neural networks B) Feedforward neural networks
A) Static B) Deterministic C) Dynamic
A) human perceive everything as a pattern while machine perceive it merely as data B) human have more IQ & intellect C) human have sense organs D) human have emotions
A) brain B) nucleus C) axon D) neuron
A) the system recalls previous reference inputs & respective ideal outputs B) the strength of neural connection get modified accordingly C) the system learns from its past mistakes |