Neural Networks MCQs
Neural Networks MCQs
Our experts have gathered these Neural Networks MCQs through research, and we hope that you will be able to see how much knowledge base you have for the subject of Neural Networks by answering these multiple-choice questions.
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1: What is adaline in neural networks?
A. Adaptive line element
B. Adaptive linear element
C. Automatic linear element
D. None of the mentioned
2: 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
3: What are 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
4: How many types of Artificial Neural Networks?
A. 3
B. 2
C. 4
D. 5
5: What does RNN Stands for?
A. Recurrent Neural Network
B. Recurring Neural Network
C. Removable Neural Network
D. None of the above
6: What is auto-association task in neural networks?
A. Predicting the future inputs
B. Related to storage & recall task
C. Find relation between 2 consecutive inputs
D. All of the above
7: 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
8: Signal transmission at synapse is a?
A. Chemical process
B. Physical process
C. Both chemical & physical process
D. None of the above
9: Operations in the neural networks can perform what kind of operations?
A. Parallel
B. Serial
C. Both parallel & serial
D. None of the above
10: A neural network is a network or circuit of neurons.
A. True
B. False
11: Neural networks can be used in different fields. such as -
A. Classification
B. Data processing
C. Compression.
D. All of the above
12: What are the types Of Neural Networks?
A. Feed-forward Neural Network
B. Radial Basis Functions (RBF) Neural Network
C. Recurrent Neural Network
D. All of the above
13: 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
14: Neural Networks consist of artificial neurons that are similar to the biological model of neurons.
A. True
B. False
15: 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
16: The Modular Neural Network (MNN) is a neural network that has .......... main branches.
A. 2
B. 4
C. 6
D. 8
17: Artificial Neural Networks are the biologically inspired simulations performed on the computer to perform certain specific tasks like
A. Clustering
B. Classification
C. Pattern Recognition
D. All of the above
18: Which of the following Neural Network architectures used for Pattern Recognition?
A. Multilayer Perceptron
B. Kohonen SOM
C. Radial Basis Function Network
D. All of the above
19: 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
20: ......... types of methods are used for implementing hardware for Neural Networks.
A. 2
B. 3
C. 4
D. 5
21: A _____ measures the time a package takes to process a certain number of transactions.
A. Benchmark
B. Parameter
C. Middleware
22: What are some of the fields that the debate on intelligence spans?
A. Cognitive science, linguistics
B. Neurobiology, genetics
C. Cognitive science
D. Genetics, neuroscience
E. Philosophy, psychology, sociology
23: What has science been trying to do for decades?
A. Break the speed of light
B. Create a new form of life
C. Mimic intelligence
D. Change the weather
E. Create a time machine
24: What are AI systems that react and (appear to) reason about themselves and the world around them called?
A. Machine Learning
B. Superintelligence
C. Artificial General Intelligence
D. Robotics
E. Artificial Intelligence
25: What is the task of showing the inputs and outputs of a problem to an algorithm?
A. Data Analysis
B. Debugging
C. Machine Learning
D. Testing
26: What steps are involved between letters and fluency?
A. Practice
B. Phonetics
C. Several
D. Phonology
E. Vocabulary
27: Learning to read full sentences is an example of what type of learning?
A. Structured learning
B. Associative learning
C. Deep learning
D. Animal learning
E. Transcending mediocrity
28: What is the study of data?
A. Mathematics
B. Statistics
C. Cognitive Science
D. Data Science
29: What is another common misconception about data science?
A. That all data scientists are experts in statistics
B. That all data scientists use the same tools and techniques
C. That DL and DS are the same things
D. That data science is a recent development
E. That data scientists are only able to crunch numbers
30: What term usually refers to exploratory analysis?
A. Data mining
B. Data analysis
C. Predictive analytics
D. Analytics
31: What does NN stand for?
A. Network Node
B. Neural Network
C. Neuro-Network
D. Artificial Neural Network
32: What does a layer do?
A. Adds structure to data
B. Reduces the size of a data structure
C. Holds the results of an operation
D. Provides a way to group data
E. Defines an operation that takes some inputs, some parameters, and produces a set of outputs
33: What is the layer that receives a vector and multiplies it by a matrix?
A. Convolutional Layer
B. Convolutional Neural Network
C. Fully Connected Layer
D. Dense Layer
34: What is dense layer called?
A. A liquid that sinks to the bottom because it has more mass than water
B. A layer of neurons in a computer's cortex
C. Is the layer that receives a vector (input) and multiplies it by a matrix (parameters), producing another vector (outputs).
D. An assembly of elementary particles in a substance.
E. A thin layer sandwiched between two more dense layers.
35: A linear system is easy to what?
A. Analyze
B. Predict
C. Solve
D. Study
E. Troubleshoot
36: Why is a system non-linear?
A. It is impossible to replicate the original pattern
B. When its parts are intertwined as a complex whole
C. It is difficult to predict the outcome of a change
D. When there is feedback between the parts
E. When its components are not well-matched
37: What is a system that is non-linear?
A. An equation
B. A chord
C. A complex whole
D. A computer program
E. A computer
38: What does the word "activation" mean?
A. Process
B. Function
C. Event
D. Creation
E. Enhancement
39: Short of what is defined as ReLU(x) = max(0, x)?
A. Absolute Value
B. Linear Unit
C. Absolute Max
D. Linear Regression
E. Rectified Linear Unity
40: What is a ReLu function defined as?
A. ReLU(x) = max(0, x)
B. ReLU(x) = -1
C. ReLU(x) = 0
D. ReLu(x) = 1 - x
41: What is the purpose of non-linearities?
A. To produce signals that are different from the standard ones
B. To generate energy
C. Glue that creates a powerful model out of ordinary parts
D. To keep a machine from over-reaching
E. To make a part that can change its shape
42: Who created the perceptron model?
A. Marvin Minsky
B. Frank Rosenblatt
C. Gordon Moore
43: In what year was the perceptron created?
A. 1946
B. 1960
C. 1958
D. 1959
E. 1949
44: What is a type of math that can be performed on input from many perceptrons at once?
A. Dense layer
B. Convolutional layer
C. Fully connected layer
45: How many layers can be created by feeding a dense layer into another?
A. Four
B. Two
C. Three
D. Six
46: What does a model define as an operation?
A. Statistics
B. Matrix operations
C. Weights
D. Means
E. Dimensions
47: What term describes the actual process of learning itself?
A. Learning
B. Education
C. Training
48: What is a function that measures the "wrongness" of a model?
A. Fit
B. Loss
C. Accuracy
49: What does "∇L" indicate?
A. A complex function of one real variable
B. How the loss changes as θ changes
C. The derivative of the loss function
D. A vector field on R
E. The gradient of the loss function
50: What do we typically train our model for?
A. Hundreds to thousands of epochs
B. Thousands to tens of thousands of epochs
C. Fewer than 10 epochs
D. Tens to hundreds of epochs
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Neural Networks MCQs
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