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Neural Networks MCQ

What kind of connection carries input information?

Input connections

Input/Output connections

Output connections

Communication connections

Answer

Correct Answer: Input connections

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A negative connection weight reduces what?

Sentiment

Activity

Load

Speed

Accuracy

Answer

Correct Answer: Activity

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An inhibitory connection acts as what to reduce the activity of its input neurons?

A dendrite

A negative connection weight

A strengthening connection weight

A connection between neurons

A connection between axons and dendrites

Answer

Correct Answer: A negative connection weight

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What is the effect of a negative connection weight on the summed activity of a neuron receiving activity?

Reducing that activity

No effect

Increasing that activity

Answer

Correct Answer: Reducing that activity

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What does Bopfield mean?

A total order in a partially ordered set

A network with symmetric connection weights

A DAG with no cycles

A supervised learning algorithm

Answer

Correct Answer: A network with symmetric connection weights

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What type of connection weights does the Bopfield network have?

Convex

Asymmetric

Symmetric

Linear

Answer

Correct Answer: Symmetric

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What do the outputs of the neurons in the Bidden layer only go to?

The output layer

Other neurons

The input layer

Answer

Correct Answer: Other neurons

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What does the activity of an input neuron modify in the Bebb learning law?

Surplus gain

Output function

Connection weight

Activation function

Answer

Correct Answer: Connection weight

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What learning is based on modification of connection weights?

Aversive learning

Bebb learning law

Connectionist learning

Reinforcement learning

Answer

Correct Answer: Bebb learning law

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What type of modification do gradient descent have?

Error estimates

Decrease

Increase

Connection weights

Activation functions

Answer

Correct Answer: Connection weights

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What is the modification of connection weights called?

Stochastic gradient descent

Gradient descent

Auto-regressive cross-validation

Weight decay

Conjugate gradients

Answer

Correct Answer: Gradient descent

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What type of function could have a minimum?

Gaussian function

Discrete function

Energy or error

Area or diameter

Volume or weight

Answer

Correct Answer: Energy or error

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What type of response does a neural network give when it has never been exposed to its input?

Correct

Incorrect

Mixed

Random

Answer

Correct Answer: Correct

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Neural networks can give a correct response to what they have been exposed to previously?

Inputs

Error rates

Outputs

Data

Answer

Correct Answer: Inputs

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What does the term "approximation" mean to a neural network?

The amount of error a neural network makes when approximating a given function

The ability of a neural network to approximate the output of another neural network

The ability of a neural network to approximate a given function

The use of a differentiable function approximation algorithm

The process of training a neural network to approximate a given function

Answer

Correct Answer: The ability of a neural network to approximate a given function

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What is the name of the ability of a neural network to approximate a given function?

Supervised learning

Superposition

Unsupervised learning

Approximation

Reproduction

Answer

Correct Answer: Approximation

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What is the ability of a neural network to approximate?

To minimize error

A given function

The ability of a mathematician to approximate

The ability of a human to approximate

Answer

Correct Answer: A given function

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How are simplified models of real neurons different from real neurons?

Voluntary muscles: simplified models of real neurons

Real neurons: simplified models of real neurons

Have less dendritic branches

Formal neurons: simplified models of real neurons

More complex than real neurons

Answer

Correct Answer: Formal neurons: simplified models of real neurons

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What are simplified models of real neurons called?

Spiking neurons

Functional neurons

Connectional neurons

Formal neurons

Answer

Correct Answer: Formal neurons

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A vector composed of what?

A set of features

Data

Tensors

Numbers

A list of strings

Answer

Correct Answer: A set of features

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What is the name of the vector composed of features produced by a layer of feature detecting neurons?

Output vector

Input vector

Feature vector

Answer

Correct Answer: Feature vector

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What do detection neurons do?

Detect the presence of a specific stimulus

Detect changes in the input

Detect the presence of a specific pattern

Detect significant aspects of the input

Detect when a specific rule has been violated

Answer

Correct Answer: Detect significant aspects of the input

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What are neurons trained to detect?

Stimuli in a specific location

Significant aspects of the input

Minor aspects of the input

Everything that is inputted

Answer

Correct Answer: Significant aspects of the input

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What type of neurons are trained to detect significant aspects of the input?

Feature detection neurons

Memory cells

Time-series neuron

Probabilistic neuron

Pattern recognition neurons

Answer

Correct Answer: Feature detection neurons

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What is an example of an excitatory connection?

The speed at which electrical signals travel through a neuron

The effect a neuron has on the activity of other neurons

A neuron that is receiving synaptic input

The effect a positive connection weight has on the summed activity of a neuron

A synapse

Answer

Correct Answer: The effect a positive connection weight has on the summed activity of a neuron

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What property of excitatory connections increases the summed activity of a neuron?

Number of synapses

Increased the number of presynaptic receptors

Positive connection weight

Increased the firing rate

Large dendritic size

Answer

Correct Answer: Positive connection weight

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What kind of weight is added to excitatory inputs so that the summed activity of the neuron increases?

No weight is added

Negative

Positive

Answer

Correct Answer: Positive

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In the space of connection weights, what is the error surface?

The error function

A curve

The sensitivity surface

A set of points

The surface

Answer

Correct Answer: The surface

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What is the error surface called?

The surface in the space of neurons

The surface in the space of activation functions

The surface in the space of gradient errors

The surface in the space of connection weights

The surface in the space of error estimates

Answer

Correct Answer: The surface in the space of connection weights

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What does "the numerical assignment of a quantity indicating the stability of a neural net state" mean?

Gradient descent

Error gradient

Energy function

Error function

Answer

Correct Answer: Energy function

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What is a neural net state?

The numerical assignment of a quantity indicating the stability of a neural

The result of an artificial intelligence algorithm

The activation level of a neural net unit

The sum total of the weights of all neurons in a neural net

The output of a neural net

Answer

Correct Answer: The numerical assignment of a quantity indicating the stability of a neural

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What is the storage of information in neural networks dependent on?

Number of neurons in the net

Distribution of connection weights across the net

Type of neurons in the net

Number of layers in the network

Number of training data points

Answer

Correct Answer: Distribution of connection weights across the net

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What is the storage of information in a neural network in a manner that depends on the distribution of connection weights across the net?

Distributed storage

Centralized storage

Local storage

Answer

Correct Answer: Distributed storage

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What process allow for an increase in a neuron's surface area?

Dendrites

Neurons

Synapses

Answer

Correct Answer: Dendrites

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What is the rule where weights are changed proportionally to the difference between actual output and desired output?

Law of Diminishing Returns

Delta rule

Quadratic rule

Linear rule

Cobb-Douglas rule

Answer

Correct Answer: Delta rule

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What is the name of the parameter that is used to give more or less importance to an input coming from another?

Input weight

Output weight

Connection threshold

Connection weight

Answer

Correct Answer: Connection weight

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Learning to an input means increasing the input of which neuron?

Neurons in the somatosensory cortex

Neurons in the frontal lobe

Neurons that fire most often

Most active

Answer

Correct Answer: Most active

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What is it called when training the neurons in a certain order?

Repetitive learning

Procedural learning

Competitive learning

Associative learning

Answer

Correct Answer: Competitive learning

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What is one example of a neural net that can be trained to learn digits or letters?

Text recognition

Sentence recognition

Speech recognition

Character recognition

Image recognition

Answer

Correct Answer: Character recognition

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What is a "Boltzmann machine" an algorithm for learning?

The probability distribution on a set of

A computer program that predicts the value of a random variable

A machine that calculates the probability of outcomes from a set of

A learning algorithm that uses the Boltzmann distribution to

Answer

Correct Answer: The probability distribution on a set of

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What is the algorithm used to learn the probability distribution on a set of inputs by means of weight changes using noisy responses?

Gradient descent

Boltzmann machine

Maximum entropy estimator

Support vector machine

Bayesian inference

Answer

Correct Answer: Boltzmann machine

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If a neuron has a bipolar vector, what does it mean?

It can fire either a +1 or a -1

The neuron is firing both positive and negative signals

Either -1 (inactive) or + 1 (active)

It can fire at either +1 or -1

The neuron is fireing one signal only

Answer

Correct Answer: Either -1 (inactive) or + 1 (active)

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If the output of a neuron is active, what is the value of a bipolar vector if the neuron is not in a circuit?

+ 1

-1

Answer

Correct Answer: + 1

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What does a thresholded neuron's binary output indicate?

The neuron is stimulated by a certain frequency

Either 0 (inactive) or 1 (active)

Whether or not a certain neuron is firing

Whether or not a certain stimulus is being processed

The neuron is

Answer

Correct Answer: Either 0 (inactive) or 1 (active)

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What is the output of a thresholded neuron if it is active?

0.5

-1

Answer

Correct Answer: 1

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What does a binary decision neuron respond to?

Its input signal

The strength of its input signal

Its total activity

The difference between its input and output

The average of its input and output

Answer

Correct Answer: Its total activity

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What line allows for identification of the neuron threshold as the weight on a special constant input?

Output line

Neuron input line

Bias line

Input line

Sensitization line

Answer

Correct Answer: Bias line

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A bias line is a term that allows for what?

Identification of the neuron response

Identification of the neuron threshold

Determining the location of a neuron

Identification of the neuron location

Determination of neuron activation level

Answer

Correct Answer: Identification of the neuron threshold

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What kind of error could be propagated through the network?

Broadcast error

Consensus error

Data-loss error

Forward-error

Back-error

Answer

Correct Answer: Back-error

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What is the process of allowing the error between output and desired output to be carried back through a feedforward net so as to allow updating of weights on hidden neurons?

Error-back propagation

Weight updating

Back-error propagation

Weight adjustment

Error correction

Answer

Correct Answer: Back-error propagation

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What does a network provide the completion of?

Ability to share data

Ability to share resources

Noisy input pattern

Faulty communication

Redundant resources

Answer

Correct Answer: Noisy input pattern

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What can process one part of an input?

Natural language processing

Generative adversarial networks

A computer with a scanner

Mechanical arms

Attentional neurocomputers

Answer

Correct Answer: Attentional neurocomputers

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What type of neurocomputers are not entirely focused on one part of an input?

Prototype computers

Procedural

Attentional

Neural networks

Autonomous

Answer

Correct Answer: Attentional

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What is a possible result of an associative network's output for a given input?

A random output

A set of all outputs

A certain output

No output

An empty list

Answer

Correct Answer: A certain output

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What is a property of associative networks?

Ability to find the shortest path between any two points

Ability to associate different inputs with different outputs

Ability to represent multiple inputs as a single output

Ability to map input onto output

One which gives a certain output for a given input

Answer

Correct Answer: One which gives a certain output for a given input

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What is a network that gives a certain output for a given input?

Associative network

Congruent network

Community network

Encoder-decoder network

Social network

Answer

Correct Answer: Associative network

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What does the word architecture mean?

A plan or model for organizing the elements of a work of art

The art of designing and constructing buildings

The study or application of the structure of things

The manner of connection of neurons to make a specific neural network

Answer

Correct Answer: The manner of connection of neurons to make a specific neural network

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What is the manner of connection of neurons to make a specific neural network?

Function

Transmission

Synapse

Connection

Architecture

Answer

Correct Answer: Architecture

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What is a common example of an adaptive network?

Classifying email message content into categories

Detecting fraudulent activity on a web server

Classifying data into mutually exclusive groups

Separating visual patterns into two or more classes

Detecting changes in a network over time

Answer

Correct Answer: Separating visual patterns into two or more classes

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What is the name of a type of network that can be trained to solve a given task?

Fully connected network

Adaptive network

Convolutional network

Recurrent neural network

Answer

Correct Answer: Adaptive network

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What are adaptive coefficients called?

A numeric value that describes how well a machine can generalize from one example to another

A type of filter which helps to improve image quality

A threshold which defines the minimum number of examples needed for a classification to be accurate

A mathematical function that helps to predict future events

Weights which can be modified by one of a range oflearning rules

Answer

Correct Answer: Weights which can be modified by one of a range oflearning rules

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What is the amount of electric potential arriving at a neuron?

The total amount

Alternating current

The average amount

The net amount

Direct current

Answer

Correct Answer: The net amount

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How do you activate a neuron?

To cause a neuron to fire

To make a decision

To cause a neuron to respond

To increase the activity of a neuron

To send a message

Answer

Correct Answer: To cause a neuron to respond

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What do neurons respond to?

Deactivate

Excite

Activate

Stimulate

Protect

Answer

Correct Answer: Activate

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At which time does a network absorb the absorbing state?

Random time

Large time

Instantaneous

After the network is created

End time

Answer

Correct Answer: Large time

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What term did we not even discuss?

Function

Loss function

Source

Optimization

Sink

Answer

Correct Answer: Loss function

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What is another term for convolutional neural networks?

Convolutional kernels

Neural networks

Convolutional neural networks

Convolutional layer

Convolutions

Answer

Correct Answer: Convolutions

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What is a type of neural network that has many different names?

Convolutional neural networks

Long short-term memory networks

Restricted Boltzmann machines

Recurrent neural networks

Recursive neural networks

Answer

Correct Answer: Convolutional neural networks

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What do batches divide our examples into?

Sixteen

Four

Ten

Eight

Answer

Correct Answer: Sixteen

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How many examples can be divided into sixteen batches of 64 elements?

Thousand

Hundred

Ten thousand

Ten

Sixteen thousand

Answer

Correct Answer: Thousand

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What is the name of the parameter used to adjust the rate at which the algorithm updates weights during training?

Momentum

Accuracy

Bias

Learning rate

Weight decay

Answer

Correct Answer: Learning rate

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Learning rate is often a parameter of the what?

Model

Algorithm

Optimizer

Method

Answer

Correct Answer: Optimizer

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What 95% of the time is used by people?

Eve

Adam

Earth

Answer

Correct Answer: Adam

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What percentage of the time do people use Adam?

25%

50%

95%

Answer

Correct Answer: 95%

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The complete version of backpropagation has the added complexity that each layer has its own what?

Output

Activation function

Error

Gradient

Answer

Correct Answer: Gradient

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How long do epochs typically last?

Thousands to millions

Tens to hundreds

Hundreds to thousands

Answer

Correct Answer: Tens to hundreds

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What do we typically train our model for?

Hundreds to thousands of epochs

Thousands to tens of thousands of epochs

Fewer than 10 epochs

Tens to hundreds of epochs

Answer

Correct Answer: Tens to hundreds of epochs

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What does "∇L" indicate?

A complex function of one real variable

How the loss changes as θ changes

The derivative of the loss function

A vector field on R

The gradient of the loss function

Answer

Correct Answer: How the loss changes as θ changes

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What is a function that measures the "wrongness" of a model?

Fit

Loss

Accuracy

Answer

Correct Answer: Loss

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What term describes the actual process of learning itself?

Learning

Education

Training

Answer

Correct Answer: Training

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What does a model define as an operation?

Statistics

Matrix operations

Weights

Means

Dimensions

Answer

Correct Answer: Weights

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How many layers can be created by feeding a dense layer into another?

Four

Two

Three

Six

Answer

Correct Answer: Two

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What is a type of math that can be performed on input from many perceptrons at once?

Dense layer

Convolutional layer

Fully connected layer

Answer

Correct Answer: Dense layer

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In what year was the perceptron created?

1946

1960

1958

1959

1949

Answer

Correct Answer: 1958

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Who created the perceptron model?

Marvin Minsky

Frank Rosenblatt

Gordon Moore

Answer

Correct Answer: Frank Rosenblatt

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What is the purpose of non-linearities?

To produce signals that are different from the standard ones

To generate energy

Glue that creates a powerful model out of ordinary parts

To keep a machine from over-reaching

To make a part that can change its shape

Answer

Correct Answer: Glue that creates a powerful model out of ordinary parts

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What is a ReLu function defined as?

ReLU(x) = max(0, x)

ReLU(x) = -1

ReLU(x) = 0

ReLu(x) = 1 - x

Answer

Correct Answer: ReLU(x) = max(0, x)

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Short of what is defined as ReLU(x) = max(0, x)?

Absolute Value

Linear Unit

Absolute Max

Linear Regression

Rectified Linear Unity

Answer

Correct Answer: Rectified Linear Unity

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What does the word "activation" mean?

Process

Function

Event

Creation

Enhancement

Answer

Correct Answer: Function

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What is a system that is non-linear?

An equation

A chord

A complex whole

A computer program

A computer

Answer

Correct Answer: A complex whole

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Why is a system non-linear?

It is impossible to replicate the original pattern

When its parts are intertwined as a complex whole

It is difficult to predict the outcome of a change

When there is feedback between the parts

When its components are not well-matched

Answer

Correct Answer: When its parts are intertwined as a complex whole

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A linear system is easy to what?

Analyze

Predict

Solve

Study

Troubleshoot

Answer

Correct Answer: Study

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What is dense layer called?

A liquid that sinks to the bottom because it has more mass than water

A layer of neurons in a computer's cortex

Is the layer that receives a vector (input) and multiplies it by a matrix (parameters), producing another vector (outputs).

An assembly of elementary particles in a substance.

A thin layer sandwiched between two more dense layers.

Answer

Correct Answer: Is the layer that receives a vector (input) and multiplies it by a matrix (parameters), producing another vector (outputs).

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What is the layer that receives a vector and multiplies it by a matrix?

Convolutional Layer

Convolutional Neural Network

Fully Connected Layer

Dense Layer

Answer

Correct Answer: Dense Layer

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What does a layer do?

Adds structure to data

Reduces the size of a data structure

Holds the results of an operation

Provides a way to group data

Defines an operation that takes some inputs, some parameters, and produces a set of outputs

Answer

Correct Answer: Defines an operation that takes some inputs, some parameters, and produces a set of outputs

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What does NN stand for?

Network Node

Neural Network

Neuro-Network

Artificial Neural Network

Answer

Correct Answer: Artificial Neural Network

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What term usually refers to exploratory analysis?

Data mining

Data analysis

Predictive analytics

Analytics

Answer

Correct Answer: Analytics

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What is another common misconception about data science?

That all data scientists are experts in statistics

That all data scientists use the same tools and techniques

That DL and DS are the same things

That data science is a recent development

That data scientists are only able to crunch numbers

Answer

Correct Answer: That DL and DS are the same things

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What is the study of data?

Mathematics

Statistics

Cognitive Science

Data Science

Answer

Correct Answer: Data Science

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Learning to read full sentences is an example of what type of learning?

Structured learning

Associative learning

Deep learning

Animal learning

Transcending mediocrity

Answer

Correct Answer: Deep learning

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What steps are involved between letters and fluency?

Practice

Phonetics

Several

Phonology

Vocabulary

Answer

Correct Answer: Several

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What is the task of showing the inputs and outputs of a problem to an algorithm?

Data Analysis

Debugging

Machine Learning

Testing

Answer

Correct Answer: Machine Learning

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What are AI systems that react and (appear to) reason about themselves and the world around them called?

Machine Learning

Superintelligence

Artificial General Intelligence

Robotics

Artificial Intelligence

Answer

Correct Answer: Artificial Intelligence

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What has science been trying to do for decades?

Break the speed of light

Create a new form of life

Mimic intelligence

Change the weather

Create a time machine

Answer

Correct Answer: Mimic intelligence

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What are some of the fields that the debate on intelligence spans?

Cognitive science, linguistics

Neurobiology, genetics

Cognitive science

Genetics, neuroscience

Philosophy, psychology, sociology

Answer

Correct Answer: Philosophy, psychology, sociology

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