A) TRUE B) FALSE
A) Pattern recognition B) Classification C) All of these D) Clustering
A) IF-The-Else Analysis Questions B) For Loop questions C) What-if question
A) Fault tolerance B) Adaptive Learning C) Self Organization D) Robustness
A) What-If Analysis B) Supervised Learning C) Self Organization D) Adaptive Learning
A) nodes or neurons B) axons C) Soma D) weights
A) bias B) weights C) neurons D) activation function
A) TRUE B) FALSE
A) activation or activity level of neuron B) Weight C) Bias D) None of these
A) one B) none C) multiple D) any number of
A) Recurrent neural network B) Perceptrons C) Multi layered perceptron D) Self organizing maps
A) Reinforcement learning B) Active learning C) Supervised learning D) Unsupervised learning
A) No specific Inputs are given B) Both inputs and outputs are given C) specific output values are not given D) Specific output values are given
A) Linear Functions B) Discrete Functions C) Exponential Functions D) Nonlinear 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 perceive everything as a pattern while machine perceive it merely as data B) human have emotions C) human have more IQ & intellect D) human have sense organs
A) brain B) neuron C) nucleus D) axon
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 |