Question Statement Solution
with step wise marking
"Select a 4-input neuron weighs 1, 2, 3, 4. The
transfer function here is linear, and the constant of proportionality is
equivalent to 2. Also, the inputs here are 4, 10, 5, 20, respectively. Thus,
the output would be: a. 119
b. 123
c. 238
d. 76" c 238
"Every connection link present in ANN gets linked to
the ________ that consists of various statics about an input signal.
a. Activation function
b. Neurons
c. Bias
d. Weights
" (d)
Weights
"Neural network can be Classified with computing as
A. mimics human behaviour
B. information processing paradigm
C. both a and b
D. none of the above
" both
a and b
"While interpretation of Artificial neural network, one
can say it is used for
A. pattern recognition
B. classification
C. clustering
D. all of the above
" all
of the above
"Classify the issues on which biological networks
proves to be superior than AI networks?
a) robustness & fault tolerance
b) flexibility
c) collective computation
d) all of the mentioned
" "Answer:
d
Explanation: AI network should be all of the above
mentioned."
"While inferring the neural network concept, Signal
transmission at synapse is a?
a) physical process
b) chemical process
c) physical & chemical both
d) none of the mentioned
" "Answer:
b
Explanation: Since chemicals are involved at synapse, so its
an chemical process."
"Classifying the fundamental function of dendrites in
neural network Skelton ?
a) receptors
b) transmitter
c) both receptor & transmitter
d) none of the mentioned
" Answer:
a Explanation: Dendrites are tree like projections whose function is only to
receive impulse.
"Inferring basic architecture of ANN, which is true for
neural networks?
a) It has
set of nodes and connections
b) Each node
computes it’s weighted input
c) Node could
be in excited state or non-excited state
d) All of
the above
" Answer:
All of the above
"Compare and explain, what is plasticity in neural
networks?
a) input
pattern has become static
b) input
pattern keeps on changing
c) output
pattern keeps on changing
d) none of
the above
" ans:
input pattern keeps on changing
"
Which application out of these of robots can be made of
single layer feedforward network?
a) wall climbing
b) rotating arm and legs
c) gesture control
d) wall following
" Answer:
d Explanation: Wall folloing is a simple task and doesn’t require any feedback.
"What is objective of linear autoassociative
feedforward networks?
a) to associate a given pattern with itself
b) to associate a given pattern with others
c) to associate output with input
d) none of the mentioned
" "Answer:
a
Explanation: The objective of linear autoassociative
feedforward networks is to associate a given pattern with itself."
"What property should a feedback network have, to make
it useful for storing information?
a) accretive behaviour
b) interpolative behaviour
c) both accretive and interpolative behaviour
d) none of the mentioned
" "Answer:
a
Explanation: During recall accretive behaviour make it
possible for system to store information."
"Linear neurons can be useful for application such as
interpolation, is it true?
a) yes
b) no
" "Answer:
a
Explanation: This means for input vector x, output vector y
is produced and for input a.x, output will be a.y."
"Why do we need biological neural networks?
To make smart human interactive & user friendly system
To apply heuristic search methods to find solutions of
problem
To solve tasks like machine vision & natural language
processing
All of the above
" "Answer
: D
Explanation: To make smart human interactive & user
friendly system, to apply heuristic search methods to find solutions of
problem, to solve tasks like machine vision & natural language processing
are the basic aims that a neural network achieve."
"Compare and explain, what A Neural Network can answer?
a) For Loop
questions
b) what-if
questions
c) IF-The-Else
Analysis Questions
d) None of
the mentioned
" "Answer
: B
Explanation: A Neural Network can answer what-if questions.
So, option B is correct."
"Explain, Which of the following is true for neural
networks?
a) It has a
set of nodes and connections
b) A node
could be in an excited state or non-excited state
c) Each node
computes it’s weighted input
d) All of
the above
" "Answer
: D
Explanation: Neural networks has a set of nodes and
connections where each node computes it’s weighted input and a node could be in
an excited state or non-excited state. So all of the above is correct."
"As compare to ANN, which of the following is an
example of unsupervised neural network?
a) Back-propagation
network
b) Hebb
network
c) Associative
memory network
d) Self-organizing
feature map
" Ans:
Self organizing feature map
"During illustrating the active functions in ANN,
Positive sign of weight indicates?
a) excitatory input
b) inhibitory input
c) can be either excitatory or inhibitory as such
d) none of the mentioned
" "Answer:
a
Explanation: Sign convention of neuron."
"The amount of output of one unit received by another
unit depends on what?
a) output unit
b) input unit
c) activation value
d) weight
" "Answer:
d
Explanation: Activation is sum of wieghted sum of inputs,
which gives desired output..hence output depends on weights."
"In neural network, how can connections between
different layers be achieved?
a) interlayer
b) intralayer
c) both interlayer and intralayer
d) either interlayer or intralayer
" "Answer:
c
Explanation: Connections between layers can be made to one
unit to another and within the units of a layer"
"A 4-input neuron has weights 1, 2, 3 and 4. The
transfer function is linear with the constant of proportionality being equal to
2. The inputs are 4, 10, 5 and 20 respectively. The output will be:
A. 238
B. 76
C. 119
D. 123
" Ans:
A. 238
"A perceptron adds up all the weighted inputs it
receives, and if it exceeds a certain value, it outputs a 1, otherwise it just
outputs a 0.
A. true
B. false
C. sometimes – it can also output intermediate values as
well
D. can’t say
" Ans:
A. true
"A perceptron is: The perceptron is a single layer
feed-forward neural network. It is not an autoassociative network because it
has no feedback and is not a multiple layer neural network because the
preprocessing stage is not made of neurons.
(a) a single layer feed-forward neural network with
preprocessing
(b) an autoassociative neural network
(c) a double layer autoassociative neural network
" The answer is (a).
"What are the issues on which biological networks
proves to be superior than AI networks?
a) robustness & fault tolerance
b) flexibility
c) collective computation
d) all of the mentioned" d) all of the mentioned
"Explain and classify, which of the following is true
for neural networks?
(i) The training time depends on the size of the network.
(ii) Neural networks can be simulated on a conventional
computer.
(iii)Artificial neurons are identical in operation to
biological ones.
(a) all of them are true.
(b) (ii) is true.
(c) (i) and (ii) are true.
" "The
answer is (c).
The training time depends on the size of the network; the
number of neuron is greater and therefore the the number of possible 'states'
is increased. Neural networks can be simulated on a conventional computer but
the main advantage of neural networks - parallel execution - is lost.
Artificial neurons are not identical in operation to the biological ones. We
don't know yet what the real neurons do in detail."
"In artificial Intelligence (AI), an environment is
uncertain if it is ___
a) Not fully
observable and not deterministic
b) Not fully
observable or not deterministic
c) Fully
observable but not deterministic
d) Not fully
observable but deterministic
" "Ans:
Option (B) is correct.
Explanation : We say an environment is uncertain if it is
not fully observable or not deterministic [Russell Pg. 43]."
"Which of the following is/are Common uses of RNNs?
A. Businesses Help securities traders to generate analytic
reports
B. Detect fraudulent credit-card transaction
C. Provide a caption for images
D. All of the above
" "Ans
: D
Explanation: All of the above are Common uses of RNNs."
"CNN is mostly used when there is an?
A. structured data
B. unstructured data
C. Both A and B
D. None of the above
" "Ans
: B
Explanation: CNN is mostly used when there is an
unstructured data set (e.g., images) and the practitioners need to extract
information from it."
"Which neural network has only one hidden layer between
the input and output?
A. Shallow neural network
B. Deep neural network
C. Feed-forward neural networks
D. Recurrent neural networks
" "Ans
: A
Explanation: Shallow neural network: The Shallow neural
network has only one hidden layer between the input and output."
"Which is the following is true about neurons?
A. A neuron has a
single input and only single output
B. A neuron has
multiple inputs and multiple outputs
C. A neuron has a
single input and multiple outputs
D. All of the above
" Ans:
D
"Which of the following statement is not correct?
A. Neural networks
mimic the human brain
B. It can only work
for a single input and a single output
C. It can be used in
image processing
D. None
" Ans:
B
"Which of the following steps can be taken to prevent
overfitting in a neural network?
A. Dropout of neurons
B. Early stopping
C. Batch
normalization
D. All of the above
" Ans:
D
"Locate and classify, Neural networks can be used in-
A. Regression problems
B. Classification
problems
C. Clustering
problems
D. All of the above
" Ans:
D
"In a classification problem, which of the following
activation function is most widely used in the output layer of neural networks?
A. Sigmoid function
B. Hyperbolic
function
C. Rectifier function
D. All of the above
" Ans:
A
"Which of the following is true about bias?
A. Bias is inherent
in any predictive model
B. Bias impacts the
output of the neurons
C. Both A and B
D. None
" Ans:
C
"Which of the following is a loss function?
A. Sigmoid function
B. Cross entropy
C. ReLu
D. All of the above
" Ans:
B
"Suppose you have a dataset from where you have to
predict three classes. Then which of the following configuration you should use
in the output layer?
A. Activation
function = softmax, loss function = cross entropy
B. Activation
function = sigmoid, loss function = cross entropy
C. Activation
function = softmax, loss function = mean squared error
D. Activation
function = sigmoid, loss function = mean squared error
" Ans:
A
"Which of the following activation function can not be
used in the output layer of an image classification model?
A. ReLu
B. Softmax
C. Sigmoid
D. None
" Ans:
A
"For a binary classification problem, which of the
following activation function is used?
A. ReLu
B. Softmax
C. Sigmoid
D. None
" Ans:
C
"Which of the following makes a neural network
non-linear?
A. Convolution
function
B. Batch gradient
descent
C. Rectified linear
unit
D. All of the above
" Ans:
C
"In a neural network, which of the following causes the
loss not to decrease faster?
A. Stuck at a local
minima
B. High
regularization parameter
C. Slow learning rate
D. All of the above
" Ans:
D
"Suppose the number of nodes in the input layer is 5
and the hidden layer is 10. The maximum number of connections from the input
layer to the hidden layer would be-
A. More than 50
B. Less than 50
C. 50
D. None
" Ans:
C
"Report and recognize, Which of the following neural
network model has a shared weight structure?
A. Recurrent Neural
Network
B. Convolution Neural
Network
C. Both A and B
D. None
" Ans:
C
"What is adaline in neural networks?
a) adaptive
line element
b) adaptive
linear element
c) automatic
linear element
d) none of
the mentioned
" Ans:
b
"Analyze and locate, which is true for neural networks?
a) It has
set of nodes and connections
b) Each node
computes it’s weighted input
c) Node
could be in excited state or non-excited state
d) All of
the above
" Answer:
All of the above
"Identify the models in neural networks?
a) representation
of biological neural networks
b) mathematical
representation of our understanding
c) both
first & second
d) none of
the above
"
Answer: both first & second
"Operations in the neural networks can perform what
kind of operations?
a) parallel
b) serial
c) both
parallel & serial
d) none of
the above
"
Answer: both parallel & serial
"Report and recognize, Neural networks can be used in
different fields. such as -
a) Classification
b) Data
processing
c) Compression.
d) All of
the above
"
Answer: All of the above.
"Which of the following option is not the disadvantage
of Recurrent Neural Network?
a) Training
an RNN is quite a challenging task
b) Inputs of
any length can be processed in this model.
c) Exploding
and gradient vanishing is common in this model.
d) It cannot
process very long sequences if using 'tanh' or 'relu' as an activation function
"
Answer: Inputs of any length can be processed in this model.
"Report and recognize, a perceptron is:
A. a single layer feed-forward neural network with
pre-processing
B. an auto-associative neural network
C. a double layer auto-associative neural network
D. a neural network that contains feedback
" A.a
single layer feed-forward neural network with pre-processing
"Which of the following is not the promise of
artificial neural network?
A. it can explain result
B. it can survive the failure of some nodes
C. it has inherent parallelism
D. it can handle noise
" A.it
can explain result
"Report and recognize, A Neural Network can answer
A.
For Loop questions
B.
what-if questions
C.
IF-The-Else Analysis Questions
D.
None of these" "B.
what-if questions"
"What is the name of the network, which includes
backward links from the output to the inputs as well as the hidden layers?
Perceptron
Self-organizing maps
Multi-layered perceptron
Recurrent neural network" D. Recurrent neural network
"Which of the following is true for unsupervised
learning?
Some specific output values are disclosed
Some specific output values aren't disclosed
No relevant inputs value is specified
Both inputs as well outputs are specified" Both inputs as well outputs are
specified
"What is the feature of ANNs due to which they can deal
with noisy, fuzzy, inconsistent data?
a) associative nature of networks
b) distributive nature of networks
c) both associative & distributive
d) none of the mentioned
" "
Answer: c
Explanation: General characteristics of ANNs."
"What was the name of the first model which can perform
wieghted sum of inputs?
a) McCulloch-pitts neuron model
b) Marvin Minsky neuron model
c) Hopfield model of neuron
d) none of the mentioned
" "
Answer: a
Explanation: McCulloch-pitts neuron model can perform
weighted sum of inputs followed by threshold logic operation."
"The amount of output of one unit received by another
unit depends on what?
a) output unit
b) input unit
c) activation value
d) weight
" "
Answer: d
Explanation: Activation is sum of wieghted sum of inputs,
which gives desired output..hence output depends on weights."
"What is delta (error) in perceptron model of neuron?
a) error due to environmental condition
b) difference between desired & target output
c) can be both due to difference in target output or
environmental condition
d) none of the mentioned
" "
Answer: a
Explanation: All other parameters are assumed to be null
while calculatin the error in perceptron model & only difference between
desired & target output is taken into account."
"What was the main point of difference between the
adaline & perceptron model?
a) weights are compared with output
b) sensory units result is compared with output
c) analog activation value is compared with output
d) all of the mentioned
" "
Answer: c
Explanation: Analog activation value comparison with
output,instead of desired output as in perceptron model was the main point of
difference between the adaline & perceptron model."
"what is the another name of weight update rule in
adaline model based on its functionality?
a) LMS error learning law
b) gradient descent algorithm
c) both LMS error & gradient descent learning law
d) none of the mentioned
" "
Answer: c
"
"Connections across the layers in standard topologies
& among the units within a layer can be organised?
a) in feedforward manner
b) in feedback manner
c) both feedforward & feedback
d) either feedforward & feedback
" "
Answer: d
Explanation: Connections across the layers in standard
topologies can be in feedforward manner or in feedback manner but not
both."
"Locate and classify, What is perceptron?
A single layer feed-forward neural network with
pre-processing
A neural network that contains feedback
A double layer auto-associative neural network
An auto-associative neural network
" Correct
option is A
"Neural Networks are complex functions with many parameter
Linear
Non linear
Discreate
Exponential
" Correct
option is A
"The network that involves backward links from output
to the input and hidden layers is known as
Recurrent neural network
Self organizing maps
Perceptrons
Single layered perceptron
" Correct
option is A
"When a Network is ______, it loses the ability to
generalize between similar input-output patterns.
Over trained
Curve-trained
Cost fitting
Above ALL" A
"The ____ curve decreases monotonically to a minimum,
it then start to increase as the training continues.
Early stopping point
training sample
validation learning
None" C.
validation learning
"Neurons are connected in__________
Series
Parallel
A complex
Individual" ans:
B
"Locate and classify, Axons are____________
Parts of the cell body
Connections between dendrites
Neuron outputs
Neuron inputs" Ans:
C
"Locate and classify, Neural Networks are complex
______________ with many parameters.
Linear Functions
Nonlinear Functions
Discrete Functions
Exponential Functions
" Answer:
A
"Activation functions play an important role in many
ANN's
Networking
Communication
Neural Network
Internet connection
" Answer:
C
"Why is the XOR problem exceptionally interesting to
neural network researchers?
A. because it can be expressed in a way that allows you to
use a neural network
B. because it is complex binary operation that cannot be
solved using neural networks
C. because it can be solved by a single layer perceptron
D. because it is the simplest linearly inseparable problem
that exists.
" Ans:
D.because it is the simplest linearly inseparable problem that exists.
"Why are linearly separable problems of interest of
neural network researchers?
A. because they are the only class of problem that network
can solve successfully
B. because they are the only class of problem that
perceptron can solve successfully
C. because they are the only mathematical functions that are
continue
D. because they are the only mathematical functions you can
draw
" Ans:
B. because they are the only class of problem that perceptron can solve
successfully
"Select the correct option, which is true for neural
networks?
A. it has set of nodes and connections
B. each node computes it’s weighted input
C. node could be in excited state or non-excited state
D. all of the mentioned
" D.all
of the mentioned
"Outline, out the following option, artificial neural
network used for
A.
Pattern Recognition
B.
Classification
C.
Clustering
D.
All of these" "D.
All of these"
"Locate and classify, A Neural Network can answer
A.
For Loop questions
B.
what-if questions
C.
IF-The-Else Analysis Questions
D.
None of these" "B.
what-if questions"
"Which of the following is correct for the neural
network?
I. The training time is dependent on the size of the network
II. Neural networks can be simulated on the conventional
computers
III. Artificial neurons are identical in operation to a
biological one
All of the above
(ii) is true
(i) and (ii) are true
None of the above
" Answer:
c) (i) and (ii) are true
"Which of the following is not the promise of an
artificial neural network?
It can survive the failure of some nodes
It can handle noise
It can explain the result
It has inherent parallelism" It can explain the result
"What is the name of the network, which includes
backward links from the output to the inputs as well as the hidden layers?
Perceptron
Self-organizing maps
Multi-layered perceptron
Recurrent neural network" D. Recurrent neural network
"A 4-input neuron has weights 1, 2, 3 and 4. The
transfer function is linear with the constant of proportionality being equal to
2. The inputs are 4, 10, 5 and 20 respectively. The output will be:
(a) 238
(b) 76
(c) 119
(d) 52
" "The
answer is (b).
The output is found by multipling the weights with their
respective inputs, summing the results and multipling with the trasfer
function. Therefore: Output = 2 * (1*4 + 2*10 + 3*5 + 4*20) = 238"
"Why is the XOR problem exceptionally interesting to
neural network researchers?
A. because it can be expressed in a way that allows you to
use a neural network
B. because it is complex binary operation that cannot be
solved using neural networks
C. because it can be solved by a single layer perceptron
D. because it is the simplest linearly inseparable problem
that exists.
" Ans:
D.because it is the simplest linearly inseparable problem that exists.
"Illustrate and explain the back propagation best
definition?
A. it is another name given to the curvy function in the perceptron
B. it is the transmission of error back through the network
to adjust the inputs
C. it is the transmission of error back through the network
to allow weights to be adjusted so that the network can learn.
D. none of the mentioned
" Ans:
C.it is the transmission of error back through the network to allow weights to
be adjusted so that the network can learn.
"Comparing the basic structure of Neural Network with
human body, when the cell is said to be fired?
a) if potential of body reaches a steady threshold values
b) if there is impulse reaction
c) during upbeat of heart
d) none of the mentioned
" "Answer:
a
Explanation: Cell is said to be fired if & only if
potential of body reaches a certain steady threshold values."
"As compare to ANN architecture, what is equilibrium in
neural systems?
a) deviation in present state, when small perturbations
occur
b) settlement of network, when small perturbations occur
c) change in state, when small perturbations occur
d) none of the mentioned
Answer
" "Answer:
b
Explanation: Follows from basic definition of
equilibrium."
"How is pattern storage task generally accomplished?
a) by a feedback network consisting of processing units with
non linear output functions
b) by a feedback network consisting of processing units with
linear output functions
c) by a feedforward network consisting of processing units
with non linear output functions
d) by a feedforward network consisting of processing units
with linear output functions
" "Answer:
b
Explanation: Pattern storage task generally accomplished by
a feedback network consisting of processing units with non linear output
functions."
"How can false minima be reduced in case of error in
recall in feedback neural networks?
a) by providing additional units
b) by using probabilistic update
c) can be either probabilistic update or using additional
units
d) none of the mentioned
" "Answer:
b
Explanation: Hard problem can be solved by additional units
not the false minima."
"A far as different ANN networks are concerned, loops
are allowed in?
a) FeedForward
ANN
b) ForwardFeed
ANN
c) FeedBack
ANN
d) None of
the above
" "Answer
: C
Explanation: In FeedBack ANN, loops are allowed. They are
used in content addressable memories. So, option C is correct."
"Interpreted the concept of Neuron theory, what is
Neuro software?
A. A software used to analyze neurons
B. It is powerful and easy neural network
C. Designed to aid experts in real world
D. It is software used by Neurosurgeon
" "Ans
: B
Explanation: Neuro software is powerful and easy neural
network."
"Which of the following is not the promise of
artificial neural network?
A. It can explain result
B. It can survive the failure of some nodes
C. It has inherent parallelism
D. It can handle noise
View Answer
" "Ans
: A
Explanation: The artificial Neural Network (ANN) cannot
explain result."
"ln neural network, the network capacity is defined as:
a) The
traffic (tarry capacity of the network
b) The total
number of nodes in the network
c) The
number of patterns that can be stored and recalled in a network
d) None of
the above
" Ans:
The number of patterns that can be stored and recalled in a network
"Which of the following can be an application of neural
network?
a) Sales
forecasting
b) Data
validation
c) Risk
management
d) All of
the above
" Ans:
All of the above
"Supervised learning and unsupervised learning are two
board category of
a) Data
warehouse
b) DBMS
c) DBMS
d) Neural
Network
" Ans:
C
"_________ type of model, the algorithm learns from a
dataset which is labelled, and the algorithm uses the answer keys to evaluate
its accuracy on the training data.
a) Supervised
learning
b) UnSupervised
learning
c) Reinforcement
learning
d) None of
these
" Ans:
Supervised learning
"Which of the following is a representation learning
algorithm?
A) Neural network
B) Random Forest
C) k-Nearest neighbor
D) None of the above
" "Solution:
(A)
Neural network converts data in such a form that it would be
better to solve the desired problem. This is called representation learning.
"
"A neuron with 3 inputs has the weight vector [0.2 -0.1
0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4
0.2]^T then the total input to the neuron is:
a. 0.20
b. 1.0
c. 0.02
d. -1.0
" "Answer: (c).0.02
"
"How does the transmission/pulse acknowledged?
a) by lowering electric potential of neuron body
b) by raising electric potential of neuron body
c) both by lowering & raising electric potential
d) none of the mentioned
" Answer:
c
"The cell body of neuron can be analogous to what
mathematical operation?
a) summing
b) differentiator
c) integrator
d) none of the mentioned
" "Answer:
a
Explanation: Because adding of potential (due to neural
fluid) at different parts of neuron is the reason of its firing"
"Which action is faster pattern classification or
adjustment of weights in neural nets?
a) pattern classification
b) adjustment of weights
c) equal
d) either of them can be fast, depending on conditions
" "Answer:
a
Explanation: Memory is addressable, so thus pattern can be
easily classified."
"What is the feature of ANNs due to which they can deal
with noisy, fuzzy, inconsistent data?
a) associative nature of networks
b) distributive nature of networks
c) both associative & distributive
d) none of the mentioned
" "Answer:
c
Explanation: General characteristics of ANNs."
"What is asynchronous update in neural netwks?
a) output units are updated sequentially
b) output units are updated in parallel fashion
c) can be either sequentially or in parallel fashion
d) none of the mentioned
" "Answer:
a
Explanation: Output are updated at different time in the
networks"
"If two layers coincide & weights are
symmetric(wij=wji), then what is that structure called?
a) instar
b) outstar
c) autoassociative memory
d) heteroassociative memory
" "Answer:
c
Explanation: In autoassociative memory each unit is
connected to every other unit & to itself."
"Why are linearly separable problems of interest of
neural network researchers?
A. because they are the only class of problem that network
can solve successfully
B. because they are the only class of problem that
perceptron can solve successfully
C. because they are the only mathematical functions that are
continue
D. because they are the only mathematical functions you can
draw
" Ans:
B. because they are the only class of problem that perceptron can solve
successfully
"Analyze and locate,
What are the Advantages of Neural Networks?
a) It can be
performed without any problem
b) It can be
implemented in any application.
c) A neural
network learns and reprogramming is not necessary
d) All of
the above
"
Answer: All of the above
"Analyze and locate, artificial Neural Network is based
on which approach?
a) Weak
Artificial Intelligence approach
b) Cognitive
Artificial Intelligence approach
c) Strong
Artificial Intelligence approach
d) Applied
Artificial Intelligence approach
" "View
Answer: B
Explanation: Artificial Neural Network is based on Cognitive
Artificial Intelligence approach. So, option B is correct."
"Which of the following is true for neural networks?
a) It has a
set of nodes and connections
b) A node
could be in an excited state or non-excited state
c) Each node
computes it’s weighted input
d) All of
the above
" "Answer
: D
Explanation: Neural networks has a set of nodes and
connections where each node computes it’s weighted input and a node could be in
an excited state or non-excited state. So all of the above is correct."
"Backpropagation is a learning technique that adjusts
weights in the neural network by propagating weight changes
a) Backward
from sink to source
b) Forward
from source to sink
c) Backward
from sink to hidden nodes
d) Forward
from source to hidden nodes
" "Answer
: A
Explanation: Backward from sink to source."
"Analyze and locate, The fundamental unit of the neural
network is
a) Neuron
b) Brain
c) Nucleus
d) Dendrites
" "Answer
: A
Explanation: Neurons are the fundamental units of the neural
network."
"Which of the following techniques perform similar
operations as dropout in a neural network?
a) Bagging
b) Boosting
c) Stacking
d) None of
the above
" "Answer
: A
Explanation: The correct answer is bagging as dropout can be
seen as an extreme form of bagging in which each model is trained on a single
case, and each parameter of the model is very strongly regularized by sharing
it with the corresponding parameter in all the other models. So, option A is
correct."
"What is Neuro software?
a) A
software used to analyze neurons
b) It is
powerful and easy neural network
c) It is
software used by Neurosurgeon
d) Designed
to aid experts in real world
" "Answer
: B
Explanation: Neuro software is a powerful and easy neural
network."
"What is perceptron?
a) A single
layer feed-forward neural network with pre-processing
b) A neural
network that contains feedback
c) A double
layer auto-associative neural network
d) An
auto-associative neural network
" Correct
option is A
"Which of the following is true for neural networks?
i. The
training time depends on the size of the
ii. Neural
networks can be simulated on a conventional
iii. Artificial
neurons are identical in operation to biological
a) All
b) Only (ii)
c) (i) and
(ii)
d) None
" Correct
option is C
"Neural Networks are complex functions with many
parameter
a) Linear
b) Non
linear
c) Discreate
d) Exponential
" Correct
option is A
"Which of the following is true about model capacity
(where model capacity means the ability of neural network to approximate
complex functions)?
A) As number of
hidden layers increase, model capacity increases
B) As dropout ratio
increases, model capacity increases
C) As learning rate
increases, model capacity increases
D) None of these
" Ans:
(A)
"Identify the following activation function :
φ(V) = Z + (1/ 1 + exp (– x * V + Y) ),
Z, X, Y are parameters
a. Step
function
b. Ramp
function
c. Sigmoid
function
d. Gaussian
function
" Answer: (c). Sigmoid function
"A neuron with 3 inputs has the weight vector [0.2 -0.1
0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the
total input to the neuron is:
a. 0.20
b. 1.0
c. 0.02
d. -1.0
" "Answer: (c).
0.02"
"Which of the following can be used for clustering of
data ?
a. Single
layer perception
b. Multilayer
perception
c. Self
organizing map
d. Radial
basis function
" "Answer:
(c).
Self organizing map"
"Which of the following is true for neural networks?
(A). It has a set of nodes and connections
(B). Each node computes it’s weighted input
(C). A node could be in an excited state or non-excited
state
(D). All of these
" MCQ
Answer: d
"Which of the following option is not the disadvantage
of recurrent neural network?
a) Inputs of
any length can be processed in this model
b) Exploding
and gradient vanishing is common in this model
c) Training
an RNN is quite a challenging task
d) It cannot
process very long sequences if using ‘tanh’ or ‘relu’ as an activation function
" Answer:
Inputs of any length can be processed in this model
"The neural network can be used in different field.
such as
a) Data
processing
b) Classification
c) Compression
d) All of
the above
" Answer:
All of the above
"What is auto association task in neural network?
a) input
pattern keeps on changing
b) output
pattern keeps on changing
c) input
pattern has become static
d) None
" Answer:
input pattern keeps on changing
"A perceptron adds up all the weighted inputs it
receives, and if it exceeds a certain value, it outputs a 1, otherwise it just
outputs a 0.
A. true
B. false
C. sometimes – it can also output intermediate values as
well
D. can’t say
" A.true
"Which of the following is the component of learning
system?
A. goal
B. model
C. learning rules
D. all of the mentioned
" D.all
of the mentioned
"Artificial neural network used for
A.
Pattern Recognition
B.
Classification
C.
Clustering
D.
All of these" "D.
All of these"
"Backpropagation can be defined as _________
It is another name given to the curvy function in the
perceptron.
It is the transmission of errors back through the network to
adjust the inputs.
It is the transmission of error back through the network to
allow weights to be adjusted so that the network can learn.
None of the above" C.
It is the transmission of error back through the network to allow weights to be
adjusted so that the network can learn.
"Which of the following is not the promise of an
artificial neural network?
It can survive the failure of some nodes
It can handle noise
It can explain the result
It has inherent parallelism" It can explain the result
"What are the issues on which biological networks
proves to be superior than AI networks?
a) robustness & fault tolerance
b) flexibility
c) collective computation
d) all of the mentioned" d) all of the mentioned
" What is hebb’s rule of learning
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
d) none of the mentioned
" "
Answer:c
Explanation: The strength of neuron to fire in future
increases, if it is fired repeatedly."
"Operations in the neural networks can perform what
kind of operations?
a) serial
b) parallel
c) serial or parallel
d) none of the mentioned
" "Answer:
c
Explanation: General characteristics of neural
networks."
"Which action is faster pattern classification or
adjustment of weights in neural nets?
a) pattern classification
b) adjustment of weights
c) equal
d) either of them can be fast, depending on conditions
" "Answer:
a
Explanation: Memory is addressable, so thus pattern can be
easily classified."
"Classify and select from the given option, what is an
activation value?
a) weighted sum of inputs
b) threshold value
c) main input to neuron
d) none of the mentioned
" "
Answer: a
Explanation: It is definition of activation value & is
basic q&a."
"Predict and select, Positive sign of weight indicates?
a) excitatory input
b) inhibitory input
c) can be either excitatory or inhibitory as such
d) none of the mentioned
" "
Answer: a
Explanation: Sign convention of neuron."
"Explain, Negative sign of weight indicates?
a) excitatory input
b) inhibitory input
c) excitatory output
d) inhibitory output
" "
Answer: b
Explanation: Sign convention of neuron."
"The process of adjusting the weight is known as?
a) activation
b) synchronisation
c) learning
d) none of the mentioned
" "
Answer: c
Explanation: Basic definition of learning in neural nets
."
"The procedure to incrementally update each of weights
in neural is referred to as?
a) synchronisation
b) learning law
c) learning algorithm
d) both learning algorithm & law
" "
Answer: d
Explanation: Basic definition of learning law in
neural."
"Classify and select from the given option, how can
output be updated in neural network?
a) synchronously
b) asynchronously
c) both synchronously & asynchronously
d) none of the mentioned
" "
Answer: c
Explanation: Output can be updated at same time or at
different time in the networks."
"What is asynchronous update in neural netwks?
a) output units are updated sequentially
b) output units are updated in parallel fashion
c) can be either sequentially or in parallel fashion
d) none of the mentioned
" "
Answer: a
Explanation: Output are updated at different time in the
networks."
"What was the 2nd stage in perceptron model called?
a) sensory units
b) summing unit
c) association unit
d) output unit
" "
Answer: c
Explanation: This was the very speciality of the perceptron
model, that is performs association mapping on outputs of he sensory
units."
"What is adaline in neural networks?
a) adaptive linear element
b) automatic linear element
c) adaptive line element
d) none of the mentioned
" "
Answer: a
Explanation: adaptive linear element is the full form of
adaline neural model."
"In neural how can connectons between different layers
be achieved?
a) interlayer
b) intralayer
c) both interlayer and intralayer
d) either interlayer or intralayer
" "
Answer: c
Explanation: Connections between layers can be made to one
unit to another and within the units of a layer."
"What is STM in neural network?
a) short topology memory
b) stimulated topology memory
c) short term memory
d) none of the mentioned
" "
Answer: c
Explanation: Full form of STM."
"State whether Hebb’s law is supervised learning or of
unsupervised type?
a) supervised
b) unsupervised
c) either supervised or unsupervised
d) can be both supervised & unsupervised
" "
Answer: b
Explanation: No desired output is required for it’s
implementation."
"Classify and select from the given option, What is the
objective of backpropagation algorithm?
To develop learning algorithm for multilayer feedforward
neural network, so that network can be trained to capture the mapping
implicitly
To develop learning algorithm for multilayer feedforward
neural network
To develop learning algorithm for single layer feedforward
neural network
All of the above
" Correct
option is A
"What is true regarding backpropagation rule?
Error in output is propagated backwards only to determine
weight updates
There is no feedback of signal at nay stage
It is also called generalized delta rule
All of the above
" Correct
option is D
"The general limitations of back propagation rule
is/are
Scaling
Slow convergence
Local minima problem
All of the above
" Correct
option is D
"In backpropagation rule, how to stop the learning
process?
No heuristic criteria exist
On basis of average gradient value
There is convergence involved
None of these
" Correct
option is B
"Hessian Matrix is will support to study of neural
networks specially for _____
Pruning
Second order
Optimization
Above ALL" D
"The Learning process may be viewed as a ______
problem.
Curve-cutting
Curve-Fitting
Over-Fitting
Over Training" B.
Curve-Fitting
"A neural network with minimum size is likely to learn
_____
Idiosyncrasies
noise in the trained data
Generalize better to new data
Above ALL" Ans:
D
"In _____, the winning neuron determines the spatial
location of a topological neighbourhood of exited neurons.
Competition
Synaptic adaptation
Cooperation
Above ALL" Ans:
C
"In order to study of neuro dynamics, we need a _____
model.
Computational
Mathematical
Scientific
Above ALL" Ans:
B
"Classify and select from the given option, Perceptions
are suitable for
Single layer only
Multi-layer only
Single and multi layer
Single neuron only
" Answer:
B
"Outline, out the following option, stochastic neural
networks is a_________
Back Propagation Algorithm
Security Algorithm
NN Algorithm
Feed forward Algorithm
" Answer:
A
"For what purpose Feedback neural networks are
primarily used?
a) classification
b) feature mapping
c) pattern mapping
d) none of the mentioned" Ans: a
"A perceptron adds up all the weighted inputs it
receives, and if it exceeds a certain value, it outputs a 1, otherwise it just
outputs a 0.
A. true
B. false
C. sometimes – it can also output intermediate values as
well
D. can’t say
" Ans:
A. true
"A perceptron is: The perceptron is a single layer
feed-forward neural network. It is not an autoassociative network because it
has no feedback and is not a multiple layer neural network because the
preprocessing stage is not made of neurons.
(a) a single layer feed-forward neural network with
preprocessing
(b) an autoassociative neural network
(c) a double layer autoassociative neural network
" The answer is (a).
"Which of the following is not the promise of
artificial neural network?
A. it can explain result
B. it can survive the failure of some nodes
C. it has inherent parallelism
D. it can handle noise
" A.it
can explain result
"A perceptron adds up all the weighted inputs it
receives, and if it exceeds a certain value, it outputs a 1, otherwise it just
outputs a 0.
A. true
B. false
C. sometimes – it can also output intermediate values as
well
D. can’t say
" A.true
"Explain in terms of Neural Network architecture, where
do the chemical reactions take place in neuron?
a) dendrites
b) axon
c) synapses
d) nucleus
" "Answer:
c
Explanation: It is a simple biological fact."
"Which of the following option is not the disadvantage
of Recurrent Neural Network?
a) Training
an RNN is quite a challenging task
b) Inputs of
any length can be processed in this model.
c) Exploding
and gradient vanishing is common in this model.
d) It cannot
process very long sequences if using 'tanh' or 'relu' as an activation function
" Answer:
Inputs of any length can be processed in this model.
"Illustrate and compare, what are pros of neural
networks over computers?
a) they have ability to learn b examples
b) they have real time high computational rates
c) they have more tolerance
d) all of the mentioned
" "Answer:
d
Explanation: Because of their parallel structure, they have
high computational rates than conventional computers, so all are true."
"1. Backpropagation
is a learning technique that adjusts weights in the neural network by
propagating weight changes
a) Backward
from sink to source
b) Forward
from source to sink
c) Backward
from sink to hidden nodes
d) Forward
from source to hidden nodes
" "Answer
: A
Explanation: Backward from sink to source."
"Back propagation is a learning technique that adjusts
weights in the neural network by propagating weight changes.
a. Forward
from source to sink
b. Backward
from sink to source
c. Forward
from source to hidden nodes
d. Backward
from sink to hidden nodes
" Ans:
b
"Back propagation is a learning technique that adjusts
weights in the neural network by propagating weight changes.
(A) Forward from
source to sink
(B) Backward from
sink to source
(C) Forward from
source to hidden
(D) Backward from since to hidden nodes" Ans: B
"Analyze and locate, which of the following is an
unsupervised neural network?
a) RBS
b) Hopfield
c) Back
propagation
d) Kohonen
" Ans:
Kohonen
"The most suitable activation function for hidden layer
a) Sigmoid
b) Softmax
c) ReLu
d) Tanh
" Ans:
ReLU
"Exemplifying the human neuron structure, what are dendrites?
a) fibers of nerves
b) nuclear projections
c) other name for nucleus
d) none of the mentioned
" Answer:
a
"The membrane which allows neural liquid to flow will?
a) never be imperturbable to neural liquid
b) regenerate & retain its original capacity
c) only the certain part get affected, while rest becomes
imperturbable again
d) none of the mentioned
" "Answer:
b
Explanation: Each cell of human body(internal) has
regenerative capacity"
"Why can’t we design a perfect neural network?
a) full operation is still not known of biological neurons
b) number of neuron is itself not precisely known
c) number of interconnection is very large & is very
complex
d) all of the mentioned
" "Answer:
d
Explanation: These are all fundamental reasons, why can’t we
design a perfect neural network"
"Illustrate and explain, what is learning signal in
this equation ∆wij= µf(wi a)aj?
a) µ
b) wi a
c) aj
d) f(wi a)
" "Answer:
d
Explanation: This the non linear representation of output of
the network."
"Which of the following is NOT true in problem solving
in artificial intelligence?
a) Implements
heuristic search technique
b) Solution
steps are not explicit
c) Knowledge
is imprecise
d) It works
on or implements repetition mechanism
"
"Which of the following is well suited for perceptual
tasks?
A. Feed-forward neural networks
B. Recurrent neural networks
C. Convolutional neural networks
D. Reinforcement Learning
" "Ans
: C
Explanation: CNN is a multi-layered neural network with a
unique architecture designed to extract increasingly complex features of the
data at each layer to determine the output. CNNs are well suited for perceptual
tasks."
"What is the purpose of a loss function?
A. Calculate the
error value of the forward network
B. Optimize the error
values according to the error rate
C. Both A and B
D. None
" Ans:
C
"In which type of neural network, the data is grouped
based on its distance from a center point?
a) Convolution
Neural Network
b) Recurrent
Neural Network
c) Modular
Neural Network
d) Radial
Basis Functions Neural Network
" Answer:
Radial Basis Functions Neural Network
"Assume that your machine has a large enough RAM
dedicated to training neural networks. Compared to using stochastic gradient
descent for your optimization, choosing a batch size that fits your RAM will
lead to::
A) a more
precise but slower update.
B) a less
precise but faster update.
C) a less
precise and slower update.
D) a more
precise and faster update.
" Correct
Answer : Option (A) : a more precise
but slower update.
"Recognize, why do we need biological neural networks?
a) To make
smart human interactive & user friendly system
b) To apply
heuristic search methods to find solutions of problem
c) To solve
tasks like machine vision & natural language processing
d) All of
the above
" "Answer:
D
Explanation: To make smart human interactive & user
friendly system, to apply heuristic search methods to find solutions of
problem, to solve tasks like machine vision & natural language processing
are the basic aims that a neural network achieve."
"An auto-associative neural network is:
a) A neural
network that contains no loops
b) A neural
network that contains feedback
c) A neural
network that has only one loop
d) A single
layer feed-forward neural network with pre-processing
" "Answer
: B
Explanation: An auto-associative neural network is a neural
network that contains feedback."
"Which of the factors affect the performance of the
learner system does not include?
a) Good data
structures
b) Representation
scheme used
c) Training
scenario
d) Type of
feedback
" Correct
option is A
"In general, to have a well-defined learning problem,
we must identity which of the following
a) The class
of tasks
b) The
measure of performance to be improved
c) The
source of experience
d) All of
the above
" Correct
option is D
"Report and recognize, which of the following does not
include different learning methods
a) Analogy
b) Introduction
c) Memorization
d) Deduction
" Correct
option is B
"Concept learning inferred a valued function from
training examples of its input and output.
a) Decimal
b) Hexadecimal
c) Boolean
d) All of
the above
" Correct
option is C
"Real-Time decisions, Game AI, Learning Tasks, Skill
Aquisition, and Robot Navigation are applications of which of the folowing
a) Supervised
Learning: Classification
b) Reinforcement
Learning
c) Unsupervised
Learning: Clustering
d) Unsupervised
Learning: Regression
" Correct
option is B
"Targetted marketing, Recommended Systems, and Customer
Segmentation are applications in which of the following
a) Supervised
Learning: Classification
b) Unsupervised
Learning: Clustering
c) Unsupervised
Learning: Regression
d) Reinforcement
Learning
" Correct
option is B
"Applications of NN (Neural Network)
A) Risk
management
B) Data
validation
C) Sales
forecasting
D) All of the
above
" Correct
option is D
"Match the following knowledge representation
techniques with their applications:
List – I List – II
(a) Frames (i) Pictorial representation of objects, their
attributes and relationships
(b) Conceptual dependencies (ii) To describe real world
stereotype events
(c) Associative networks (iii) Record like structures for
grouping closely related knowledge
(d) Scripts (iv) Structures and primitives to represent
sentences
code:
a b c d
a. (iii)
(iv) (i) (ii)
b. (iii) (iv)
(ii) (i)
c. (iv)
(iii) (i) (ii)
d. (iv)
(iii) (ii) (i)
" "Answer:
(a).
(iii) (iv) (i) (ii)"
"Which of the following neural networks uses supervised
learning?
(A) Multilayer perceptron
(B) Self organizing feature map
(C) Hopfield network
a. (A) only
b. (B) only
c. (A) and
(B) only
d. (A) and
(C) only
" "Answer: (a).
(A) only"
"Which is true for neural networks?
A. it has set of nodes and connections
B. each node computes it’s weighted input
C. node could be in excited state or non-excited state
D. all of the mentioned
" D.all
of the mentioned
"Which of the following is correct for the neural
network?
I. The training time is dependent on the size of the network
II. Neural networks can be simulated on the conventional
computers
III. Artificial neurons are identical in operation to a
biological one
All of the above
(ii) is true
(i) and (ii) are true
None of the above
" Answer:
c) (i) and (ii) are true
"Computational benefit of back propagation learning
is/are ______
Sensitivity analysis
Efficiency
Robustness
Above ALL" Ans:
D
"Classify, what is back propagation?
It is another name given to the curvy function in the
perceptron
It is the transmission of error back through the network to
adjust the inputs
It is the transmission of error back through the network to
allow weights to be adjusted so that the network can learn
None of the mentioned
" Answer:
C
"Illustrate and explain the back propagation best
definition?
A. it is another name given to the curvy function in the
perceptron
B. it is the transmission of error back through the network
to adjust the inputs
C. it is the transmission of error back through the network
to allow weights to be adjusted so that the network can learn.
D. none of the mentioned
" Ans:
C.it is the transmission of error back through the network to allow weights to
be adjusted so that the network can learn.
"Which of the following is the component of learning
system?
A. goal
B. model
C. learning rules
D. all of the mentioned
" D.all
of the mentioned
"Backpropagation can be defined as _________
It is another name given to the curvy function in the
perceptron.
It is the transmission of errors back through the network to
adjust the inputs.
It is the transmission of error back through the network to
allow weights to be adjusted so that the network can learn.
None of the above" C.
It is the transmission of error back through the network to allow weights to be
adjusted so that the network can learn.
"Outline out the following option, which of the
following is true for unsupervised learning?
Some specific output values are disclosed
Some specific output values aren't disclosed
No relevant inputs value is specified
Both inputs as well outputs are specified" Both inputs as well outputs are
specified
