1)What is the difference between the actual output and generated output known as?
Output Modulus
Accuracy
Cost
Output Difference
Answer:-Cost
(2)Recurrent Neural Networks are best suited for Text Processing.
True
False
Answer:-True
(3)Prediction Accuracy of a Neural Network depends on _______________ and ______________.
Input and Output
Weight and Bias
Linear and Logistic Function
Activation and Threshold
Answer:-Weight and Bias
(4)Recurrent Networks work best for Speech Recognition.
True
False
Answer:-True
(5)GPU stands for __________.
Graphics Processing Unit
Gradient Processing Unit
General Processing Unit
Good Processing Unit.
Answer:- Graphics Processing Unit
(6)Gradient at a given layer is the product of all gradients at the previous layers.
False
True
Answer:- True
(7)_____________________ is a Neural Nets way of classifying inputs.
Learning
Forward Propagation
Activation
Classification
Answer:- Forward Propagation
(8)Name the component of a Neural Network where the true value of the input is not observed.
Hidden Layer
Gradient Descent
Activation Function
Output Layer
Answer:- Hidden Layer
(9)________________ works best for Image Data.
AutoEncoders
Single Layer Perceptrons
Convolution Networks
Random Forest
Answer:- Convolution Networks
(10)Neural Networks Algorithms are inspired from the structure and functioning of the Human Biological Neuron.
False
True
Answer:- True
(11)In a Neural Network, all the edges and nodes have the same Weight and Bias values.
True
False
Answer:- False
(12)_______________ is a recommended Model for Pattern Recognition in Unlabeled Data.
CNN
Shallow Neural Networks
Autoencoders
RNN
Answer:- Autoencoders
(13)Process of improving the accuracy of a Neural Network is called _______________.
Forward Propagation
Cross Validation
Random Walk
Training
Answer:- Training
(14)Data Collected from Survey results is an example of ___________________.
Data
Information
Structured Data
Unstructured Data
Answer:- Structured Data
(15)A Shallow Neural Network has only one hidden layer between Input and Output layers.
False
True
Answer:- True
(16)Support Vector Machines, Naive Bayes and Logistic Regression are used for solving ___________________ problems.
Clustering
Classification
Regression
Time Series
Answer:- Classification
(17)The rate at which cost changes with respect to weight or bias is called __________________.
Derivative
Gradient
Rate of Change
Loss
(18)What does LSTM stand for?
Long Short Term Memory
Least Squares Term Memory
Least Square Time Mean
Long Short Threshold Memory
Answer:-Long Short Term Memory
(19)All the Visible Layers in a Restricted Boltzmannn Machine are connected to each other.
True
False
Answer:- False
(20)All the neurons in a convolution layer have different Weights and Biases.
True
False
Answer:- False
(21)What is the method to overcome the Decay of Information through time in RNN known as?
Back Propagation
Gradient Descent
Activation
Gating
Answer:- Gating
(22)Recurrent Network can input Sequence of Data Points and Produce a Sequence of Output.
False
True
Answer:- True
(23)A Deep Belief Network is a stack of Restricted Boltzmann Machines.
False
True
Answer:-True
(24)Restricted Boltzmann Machine expects the data to be labeled for Training.
False
True
Answer:- False
(25)What is the best Neural Network Model for Temporal Data?
Recurrent Neural Network
Convolution Neural Networks
Temporal Neural Networks
Multi Layer Perceptrons
Answer:- Recurrent Neural Network
(26)RELU stands for ______________________________.
Rectified Linear Unit
Rectified Lagrangian Unit
Regressive Linear Unit
Regressive Lagrangian Unit
Answer:- Rectified Linear Unit
(27)Why is the Pooling Layer used in a Convolution Neural Network?
They are of no use in CNN.
Dimension Reduction
Object Recognition
Image Sensing
Answer:- Dimension Reduction
(28)What are the two layers of a Restricted Boltzmann Machine called?
Input and Output Layers
Recurrent and Convolution Layers
Activation and Threshold Layers
Hidden and Visible Layers
Answer:- Hidden and Visible Layers
(29)The measure of Difference between two probability distributions is know as ________________________.
Probability Difference
Cost
KL Divergence
Error
Answer:- KL Divergence
(30)A _________________ matches or surpasses the output of an individual neuron to a visual stimuli.
Max Pooling
Gradient
Cost
Convolution
Answer:- Convolution
(31)The rate at which cost changes with respect to weight or bias is called __________________.
Derivative
Gradient
Rate of Change
Loss
Answer:- Gradient
(32)Autoencoders are trained using _____________________.
Feed Forward
Reconstruction
Back Propagation
They do not require Training
Answer:- Back Propagation
(33)How do RNTS interpret words?
One Hot Encoding
Lower Case Versions
Word Frequencies
Vector Representations
Answer:-Vector Representations
(34)De-noising and Contractive are examples of __________________.
Shallow Neural Networks
Autoencoders
Convolution Neural Networks
Recurrent Neural Networks
Answer:-Autoencoders
(35)Autoencoders cannot be used for Dimensionality Reduction.
False
True
Answer:-False
(36)Recursive Neural Tensor Nets models are best suited for Recursive Data.
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