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