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) Self Organization B) Robustness C) Fault tolerance D) Adaptive Learning
A) Adaptive Learning B) Supervised Learning C) What-If Analysis D) Self Organization
A) axons B) Soma C) weights D) nodes or neurons
A) activation function B) neurons C) bias D) weights
A) TRUE B) FALSE
A) Weight B) None of these C) Bias D) activation or activity level of neuron
A) none B) any number of C) multiple D) one
A) Self organizing maps B) Multi layered perceptron C) Recurrent neural network D) Perceptrons
A) Unsupervised learning B) Supervised learning C) Active learning D) Reinforcement learning
A) No specific Inputs are given B) specific output values are not given C) Specific output values are given D) Both inputs and outputs are given
A) Nonlinear Functions B) Discrete Functions C) Exponential Functions D) Linear Functions
A) Recurrent neural networks B) Feedforward neural networks
A) Feedforward neural networks B) Recurrent neural networks
A) Static B) Deterministic C) Dynamic
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) axon B) brain C) neuron D) nucleus
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 |