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Top 100+ Artificial Neural Network Interview Questions And Answers

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Top 100+ Artificial Neural Network Interview Questions And Answers

Question 1. What Are Neural Networks? What Are The Types Of Neural Networks?

Answer :

In easy phrases, a neural community is a connection of many very tiny processing factors referred to as as neurons. There are two types of neural community-

Biological Neural Networks–  These are made of real neurons.Those tiny CPU’s which you have were given inside your mind..If u have..Not simplest brain,,but neurons in reality make the whole apprehensive machine.

Artificial Neural Networks– Artificial Neural Networks is an imitation of Biological Neural Networks,,with the aid of artificial designing small processing factors, in lieu of using digital computing structures which have most effective the binary digits. The Artificial Neural Networks are essentially designed to make robots give the human pleasant efficiency to the work.

Question 2. Why Use Artificial Neural Networks? What Are Its Advantages?

Answer :

Mainly, Artificial Neural Networks OR Artificial Intelligence is designed to present robots human excellent questioning. So that machines can determine “What if” and ”What if not” with precision. Some of the alternative blessings are:-

Adaptive studying: Ability to discover ways to do obligations based totally at the data given for training or preliminary experience.
Self-Organization: An Artificial Neural Networks can create its own business enterprise or representation of the information it receives in the course of getting to know time.
Real Time Operation: Artificial Neural Networks computations can be performed in parallel, and unique hardware gadgets are being designed and synthetic which take advantage of this functionality.
Fault Tolerance via Redundant Information Coding: Partial destruction of a network ends in the corresponding degradation of overall performance. However, a few community competencies may be retained regardless of essential network harm.
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Question three. How Are Artificial Neural Networks Different From Normal Computers?

Answer :

Simple difference is that the Artificial Neural Networks can study via examples contrary to Normal Computers who carry out the mission on Algorithms. Although, the examples given to Artificial Neural Networks have to be cautiously chosen. Once properly “taught” Artificial Neural Networks can  do on their personal,,,or at least try to imitate..But that makes them so Unpredictable , that is contrary to that of set of rules based totally computers which we use in our every day existence.

Question 4. How Human Brain Works?

Answer :

It is bizarre at the equal time great to recognize that we honestly do not realize how we suppose.  Biologically, neurons in human brain acquire signals from host of first-class systems called as dendrites.  The neuron sends out spikes of electrical activity through an extended, thin stand known as an axon, which splits into lots of branches. At the end of every department, a structure called a synapse converts the hobby from the axon into electrical outcomes that inhibit or excite activity from the axon into electrical outcomes that inhibit or excite hobby inside the linked neurons. When a neuron receives excitation enter that is sufficiently huge compared with its inhibitory enter, it sends a spike of electrical pastime down its axon. Learning occurs through changing the effectiveness of the synapses in order that the have an effect on of 1 neuron on every other changes.

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Question five. What Is Simple Artificial Neuron?

Answer :

It is truly a processor with many inputs and one output….It works in both the Training Mode or Using Mode.  In the training mode, the neuron may be trained to hearth (or no longer), for particular enter styles. In the the usage of mode, while a taught enter sample is detected on the input, its related output becomes the present day output. If the input pattern does not belong in the taught list of enter patterns, the firing rule is used to determine whether to fireplace or not.

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Question 6. How Artificial Neurons Learns?

Answer :

This is a two paradigm procedure-

Associative Mapping:   Here the network produces a sample output by using running in a sample on the given input.
Regularity Detection:  In this, gadgets learn how to reply to precise houses of the input styles. Whereas in associative mapping the network stores the relationships among styles, in regularity detection the reaction of each unit has a selected ‘meaning’. This form of mastering mechanism is essential for feature discovery and knowledge illustration.
Question 7. List Some Commercial Practical Applications Of Artificial Neural Networks?

Answer :

Since neural networks are first-rate at figuring out patterns or tendencies in records, they are properly appropriate for prediction or forecasting desires such as:

income forecasting
industrial method manipulate
customer studies
facts validation
threat management
target marketing
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Question 8. Are Neural Networks Helpful In Medicine?

Answer :

Yes of course…

Electronic noses: ANNs are used experimentally to put in force electronic noses. Electronic noses have numerous capability packages in telemedicine. Telemedicine is the practice of drugs over lengthy distances thru a conversation hyperlink. The electronic nose would pick out odors inside the faraway surgical environment. These identified odors would then be electronically transmitted to any other site where an door era machine might recreate them. Because the sense of smell may be an crucial sense to the health care provider, telesmell might enhance telepresent surgery.
Instant Physician: An application advanced inside the mid-Eighties called the “instant health practitioner” skilled an automobile-associative reminiscence neural community to save a huge variety of clinical data, every of which includes information on signs, prognosis, and treatment for a particular case. After training, the internet can be supplied with enter which include a hard and fast of signs; it will then discover the overall saved pattern that represents the “high-quality” analysis and remedy.
Question nine. What Are The Disadvantages Of Artificial Neural Networks?

Answer :

The principal drawback is they require big diversity of education for working in a actual surroundings. Moreover, they are not sturdy enough to paintings in the real global.

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Question 10. How Artificial Neural Networks Can Be Applied In Future?

Answer :

Pen PC’s: PC’s wherein possible write on a tablet, and the writing might be diagnosed and translated into (ASCII) textual content.
White items and toys: As Neural Network chips become to be had, the possibility of easy reasonably-priced systems that have found out to apprehend easy entities (e.G. Partitions looming, or easy instructions like Go, or Stop), may additionally result in their incorporation in toys and washing machines and so forth. Already the Japanese are the usage of a related generation, fuzzy common sense, on this manner. There is sizable hobby within the mixture of fuzzy and neural technology.
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Question eleven. What Can You Do With An Nn And What Not?

Answer :

In precept, NNs can compute any computable characteristic, i.E., they could do the whole lot a everyday digital pc can do (Valiant, 1988; Siegelmann and Sontag, 1999; Orponen, 2000; Sima and Orponen, 2001), or possibly even extra, underneath a few assumptions of doubtful practicality (see Siegelmann, 1998, however additionally Hadley, 1999).

Practical applications of NNs most customarily rent supervised mastering. For supervised learning, you need to provide schooling statistics that includes each the enter and the preferred end result (the target cost). After successful education, you may gift enter facts alone to the NN (that is, enter facts with out the preferred end result), and the NN will compute an output value that approximates the desired end result. However, for schooling to be successful, you could want plenty of training statistics and plenty of pc time to do the schooling. In many programs, along with photograph and textual content processing, you will must do a variety of work to choose suitable input information and to code the data as numeric values.

In practice, NNs are particularly useful for type and feature approximation/mapping issues which are tolerant of some imprecision, that have masses of education records to be had, but to which hard and rapid guidelines (which includes those who might be used in an expert gadget) cannot without problems be implemented. Almost any finite-dimensional vector characteristic on a compact set may be approximated to arbitrary precision by way of feedforward NNs (which can be the sort most customarily utilized in sensible applications) when you have sufficient records and enough computing assets.

To be incredibly more unique, feedforward networks with a unmarried hidden layer and skilled with the aid of least-squares are statistically regular estimators of arbitrary rectangular-integrable regression functions underneath certain almost-satisfiable assumptions concerning sampling, target noise, wide variety of hidden devices, size of weights, and shape of hidden-unit activation function (White, 1990). Such networks can also gain knowledge of as statistically consistent estimators of derivatives of regression capabilities (White and Gallant, 1992) and quantiles of the conditional noise distribution (White, 1992a). Feedforward networks with a unmarried hidden layer the use of threshold or sigmoid activation features are universally consistent estimators of binary classifications (Faragó and Lugosi, 1993; Lugosi and Zeger 1995; Devroye, Györfi, and Lugosi, 1996) under similar assumptions. Note that those results are stronger than the familiar approximation theorems that simply display the existence of weights for arbitrarily correct approximations, with out demonstrating that such weights may be received by using gaining knowledge of.

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Question 12. Who Is Concerned With Nns?

Answer :

Neural Networks are thrilling for pretty a variety of very extraordinary people:

Computer scientists want to discover approximately the houses of non-symbolic records processing with neural nets and about studying systems in popular.
Statisticians use neural nets as bendy, nonlinear regression and classification models.
Engineers of many types exploit the skills of neural networks in many regions, which include signal processing and automatic manipulate.
Cognitive scientists view neural networks as a likely apparatus to explain models of questioning and cognizance (High-stage mind characteristic).
Neuro-physiologists use neural networks to explain and explore medium-level mind characteristic (e.G. Reminiscence, sensory machine, motorics).
Physicists use neural networks to model phenomena in statistical mechanics and for plenty of different obligations.
Biologists use Neural Networks to interpret nucleotide sequences.
Philosophers and a few different people may also be interested in Neural Networks for diverse reasons.
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Question thirteen. How Many Kinds Of Nns Exist?

Answer :

There are many many varieties of NNs through now. Nobody knows exactly what number of. New ones (or as a minimum variations of antique ones) are invented every week. Below is a group of some of the maximum well known strategies, not claiming to be whole.

The two major styles of mastering algorithms are supervised and unsupervised.

In supervised mastering, the correct outcomes (goal values, preferred outputs) are acknowledged and are given to the NN for the duration of education in order that the NN can alter its weights to try healthy its outputs to the target values. After schooling, the NN is examined by way of giving it handiest input values, not goal values, and seeing how near it comes to outputting the appropriate target values.
In unsupervised mastering, the NN isn't always furnished with the ideal effects during training. Unsupervised NNs generally carry out some type of information compression, which include dimensionality discount or clustering.
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Question 14. How Many Kinds Of Kohonen Networks Exist?

Answer :

Teuvo Kohonen is one of the maximum well-known and prolific researchers in neurocomputing, and he has invented a selection of networks. But many humans discuss with "Kohonen networks" without specifying which type of Kohonen community, and this lack of precision can cause confusion. The phrase "Kohonen community" most customarily refers to one of the following three varieties of networks:

VQ: Vector Quantization--competitive networks that may be considered as unsupervised density estimators or autoassociators (Kohonen, 1995/1997; Hecht-Nielsen 1990), intently associated with ok-means cluster evaluation (MacQueen, 1967; Anderberg, 1973). Each competitive unit corresponds to a cluster, the middle of that is called a "codebook vector". Kohonen's gaining knowledge of law is an on-line set of rules that unearths the codebook vector closest to each education case and movements the "triumphing" codebook vector in the direction of the schooling case.
SOM: Self-Organizing Map--aggressive networks that provide a "topological" mapping from the enter area to the clusters (Kohonen, 1995). The SOM changed into stimulated by means of the manner in which various human sensory impressions are neurologically mapped into the mind such that spatial or different family members amongst stimuli correspond to spatial relations some of the neurons. In a SOM, the neurons (clusters) are prepared right into a grid--typically -dimensional, however from time to time one-dimensional or (rarely) three- or extra-dimensional. The grid exists in a area that is break free the input space; any variety of inputs may be used as long as the range of inputs is more than the dimensionality of the grid area. A SOM tries to find clusters such that any  clusters which might be close to every different within the grid area have codebook vectors close to each other within the input space. But the speak does now not keep: codebook vectors which are close to every different within the enter area do no longer always correspond to clusters which are close to each other in the grid. Another manner to look at that is that a SOM tries to embed the grid in the enter space such each schooling case is close to some codebook vector, but the grid is bent or stretched as low as feasible. Yet another manner to have a look at it's far that a SOM is a (discretely) clean mapping among areas within the input area and factors inside the grid space. The fine manner to undestand this is to look at the photos in Kohonen (1995) or numerous other NN textbooks.
LVQ: Learning Vector Quantization--aggressive networks for supervised category (Kohonen, 1988, 1995; Ripley, 1996). Each codebook vector is assigned to one of the target instructions. Each magnificence may have one or greater codebook vectors. A case is classified via locating the nearest codebook vector and assigning the case to the class corresponding to the codebook vector. Hence LVQ is a sort of nearest-neighbor rule.
Question 15. How Are Layers Counted?

Answer :

How to matter layers is an issue of big dispute.

Some human beings count number layers of devices. But of those humans, a few count the input layer and some do not.
Some people count number layers of weights. But I don't have any idea how they be counted bypass-layer connections.
To avoid ambiguity, you should speak of a 2-hidden-layer community, now not a 4-layer community (as a few might call it) or 3-layer community (as others might name it). And if the connections comply with any pattern other than absolutely connecting each layer to the next and to no others, you need to cautiously specify the connections.

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Question sixteen. What Are Cases And Variables?

Answer :

A vector of values provided at one time to all the enter units of a neural community is called a "case", "example", "sample, "sample", and so on. The term "case" could be used on this FAQ due to the fact it is broadly recognized, unambiguous, and calls for much less typing than the opposite phrases. A case can also consist of not handiest enter values, however also target values and likely different data.

A vector of values offered at one-of-a-kind instances to a single enter unit is often referred to as an "enter variable" or "characteristic". To a statistician, it's far a "predictor", "regressor", "covariate", "independent variable", "explanatory variable", etc. A vector of target values related to a given output unit of the community all through training may be known as a "goal variable" on this FAQ. To a statistician, it's also a "response" or "based variable".

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Question 17. What Are The Population, Sample, Training Set, Design Set, Validation Set, And Test Set?

Answer :

It is rarely beneficial to have a NN definitely memorize a hard and fast of data, because memorization can be achieved a whole lot extra effectively via severa algorithms for desk look-up. Typically, you want the NN for you to perform appropriately on new facts, this is, to generalize.

There appears to be no term in the NN literature for the set of all instances which you want so one can generalize to. Statisticians call this set the "population". Tsypkin (1971) called it the "grand truth distribution," but this term has in no way stuck on.

Neither is there a steady time period inside the NN literature for the set of instances which are available for schooling and comparing an NN. Statisticians name this set the "pattern". The sample is mostly a subset of the populace.

(Neurobiologists suggest something entirely extraordinary via "population," seemingly some collection of neurons, however I even have never observed out the exact meaning. I am going to keep to use "populace" within the statistical sense until NN researchers reach a consensus on some other phrases for "population" and "sample"; I suspect this could never appear.)

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Question 18. How Are Nns Related To Statistical Methods?

Answer :

There is massive overlap between the fields of neural networks and facts. Statistics is concerned with records evaluation. In neural network terminology, statistical inference means learning to generalize from noisy facts. Some neural networks are not worried with facts analysis (e.G., the ones supposed to version organic structures) and consequently have little to do with information. Some neural networks do now not study (e.G., Hopfield nets) and consequently have little to do with facts. Some neural networks can learn correctly best from noise-unfastened information (e.G., ART or the perceptron rule) and therefore would no longer be considered statistical techniques. But most neural networks which could learn to generalize efficiently from noisy records are similar or identical to statistical strategies. For example:

Feedforward nets and not using a hidden layer (along with useful-link neural nets and better-order neural nets) are essentially generalized linear fashions.
Feedforward nets with one hidden layer are intently related to projection pursuit regression.
Probabilistic neural nets are identical to kernel discriminant evaluation.
Kohonen nets for adaptive vector quantization are very similar to k-approach cluster evaluation.
Kohonen self-organizing maps are discrete approximations to essential curves and surfaces.
Hebbian mastering is closely related to essential component analysis.
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Question 19. What Are Combination, Activation, Error, And Objective Functions?

Answer :

Combination features: Each non-enter unit in a neural network combines values that are fed into it through synaptic connections from different devices, producing a single value known as the "net input". There is not any general term inside the NN literature for the characteristic that mixes values. In this FAQ, it will be referred to as the "aggregate characteristic". The aggregate feature is a vector-to scalar characteristic. Most NNs use either a linear aggregate characteristic (as in MLPs) or a Euclidean distance aggregate feature (as in RBF networks). There is an in depth discussion of networks the use of those  forms of mixture function beneath "How do MLPs compare with RBFs?"

Activation functions: Most devices in neural networks remodel their net enter with the aid of the use of a scalar-to-scalar function called an "activation feature", yielding a price called the unit's "activation". Except probable for output devices, the activation price is fed through synpatic connections to 1 or more different gadgets. The activation function is every now and then referred to as a "switch", and activation functions with a bounded range are often referred to as "squashing" features, which includes the usually used tanh (hyperbolic tangent) and logistic (1/(1+exp(-x)))) functions. If a unit does now not rework its net input, it's miles said to have an "identification" or "linear" activation feature. The purpose for the use of non-identity activation functions is defined below "Why use activation features?"

Error functions: Most methods for education supervised networks require a degree of the discrepancy between the networks output value and the target (favored output) fee (even unsupervised networks may additionally require such a degree of discrepancy.

Question 20. What Are Batch, Incremental, On-line, Off-line, Deterministic, Stochastic, Adaptive, Instantaneous, Pattern, Constructive, And Sequential Learning?

Answer :

There are many approaches to categorize mastering strategies. The differences are overlapping and can be complicated, and the terminology is used very inconsistently. This answer attempts to impose a few order on the chaos, probable in vain.

Batch vs. Incremental Learning (also Instantaneous, Pattern, and Epoch)

Batch mastering proceeds as follows:

Initialize the weights. Repeat the following steps: Process all the training facts. Update the weights.

Incremental learning proceeds as follows:

Initialize the weights. Repeat the subsequent steps: Process one schooling case. Update the weights.

In the above sketches, the precise meaning of "Process" and "Update" depends on the specific schooling set of rules and may be quite complicated for methods along with Levenberg-Marquardt Standard backprop (see What is backprop?) is pretty easy, even though. Batch wellknown backprop (with out momentum) proceeds as follows:

Initialize the weights W. Repeat the following steps: Process all of the education information DL to compute the gradient of the common errors feature AQ(DL,W). Update the weights via subtracting the gradient times the mastering fee.

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Question 21. What Is Backprop?

Answer :

"Backprop" is short for "backpropagation of errors". The time period backpropagation reasons a whole lot confusion. Strictly speakme, backpropagation refers to the technique for computing the gradient of the case-clever errors characteristic with recognize to the weights for a feedforward community, a honest however fashionable application of the chain rule of simple calculus (Werbos 1974/1994). By extension, backpropagation or backprop refers to a education approach that makes use of backpropagation to compute the gradient. By in addition extension, a backprop community is a feedforward network trained by using backpropagation.

Question 22. What Learning Rate Should Be Used For Backprop?

Answer :

In general backprop, too low a learning rate makes the network examine very slowly. Too excessive a getting to know charge makes the weights and objective characteristic diverge, so there may be no studying in any respect. If the goal function is quadratic, as in linear models, exact learning costs can be computed from the Hessian matrix (Bertsekas and Tsitsiklis, 1996). If the goal feature has many local and global optima, as in ordinary feedforward NNs with hidden gadgets, the choicest gaining knowledge of charge often changes dramatically in the course of the training procedure, for the reason that Hessian additionally adjustments dramatically. Trying to teach a NN the usage of a constant mastering rate is often a tedious technique requiring much trial and error.

Question 23. What Are Conjugate Gradients, Levenberg-marquardt, Etc.?

Answer :

Training a neural community is, in most instances, an exercising in numerical optimization of a usually nonlinear objective function ("objective characteristic" way whatever function you are attempting to optimize and is a barely greater fashionable term than "error feature" in that it is able to encompass other portions together with penalties for weight decay;

Methods of nonlinear optimization were studied for masses of years, and there is a huge literature at the issue in fields which includes numerical evaluation, operations research, and statistical computing, e.G., Bertsekas (1995), Bertsekas and Tsitsiklis (1996), Fletcher (1987), and Gill, Murray, and Wright (1981). Masters (1995) has a very good standard dialogue of conjugate gradient and Levenberg-Marquardt algorithms inside the context of NNs.

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Question 24. How Does Ill-conditioning Affect Nn Training?

Answer :

Numerical situation is one of the most fundamental and important standards in numerical analysis. Numerical condition affects the speed and accuracy of maximum numerical algorithms. Numerical circumstance is mainly critical inside the have a look at of neural networks due to the fact unwell-conditioning is a common motive of sluggish and inaccurate consequences from backprop-type algorithms.

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Question 25. How To Avoid Overflow In The Logistic Function?

Answer :

The formulation for the logistic activation characteristic is often written as:

netoutput = 1 / (1+exp(-netinput));

But this method can produce floating-point overflow within the exponential characteristic in case you application it in this simple form. To keep away from overflow, you may try this:

if (netinput < -45) netoutput = 0; else if (netinput > 45) netoutput = 1; else netoutput = 1 / (1+exp(-netinput));

The constant forty five will work for double precision on all machines that I understand of, but there can be a few bizarre machines wherein it'll require a few adjustment. Other activation capabilities may be dealt with further.




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