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