Top 25 Artificial Neural Network Interview Questions You Must Prepare 04.Dec.2023

Neural Networks are interesting for quite a lot of very different people:

  • Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general.
  • Statistici use neural nets as flexible, nonlinear regression and classification models.
  • Engineers of many kinds exploit the capabilities of neural networks in many areas, such as signal processing and automatic control.
  • Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and consciousness (High-level brain function).
  • Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics).
  • Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks.
  • Biologists use Neural Networks to interpret nucleotide sequences.
  • Philosophers and some other people may also be interested in Neural Networks for various reasons.

In principle, NNs can compute any computable function, i.e., they can do everything a normal digital computer can do (Valiant, 1988; Siegelmann and Sontag, 1999; Orponen, 2000; Sima and Orponen, 2001), or perhaps even more, under some assumptions of doubtful practicality (see Siegelmann, 1998, but also Hadley, 1999).

Practical applications of NNs most often employ supervised learning. For supervised learning, you must provide training data that includes both the input and the desired result (the target value). After successful training, you can present input data alone to the NN (that is, input data without the desired result), and the NN will compute an output value that approximates the desired result. However, for training to be successful, you may need lots of training data and lots of computer time to do the training. In many applications, such as image and text processing, you will have to do a lot of work to select appropriate input data and to code the data as numeric values.

In practice, NNs are especially useful for classification and function approximation/mapping problems which are tolerant of some imprecision, which have lots of training data available, but to which hard and fast rules (such as those that might be used in an expert system) cannot easily be applied. Almost any finite-dimensional vector function on a compact set can be approximated to arbitrary precision by feedforward NNs (which are the type most often used in practical applications) if you have enough data and enough computing resources.

To be somewhat more precise, feedforward networks with a single hidden layer and trained by least-squares are statistically consistent estimators of arbitrary square-integrable regression functions under certain practically-satisfiable assumptions regarding sampling, target noise, number of hidden units, size of weights, and form of hidden-unit activation function (White, 1990). Such networks can also be trained as statistically consistent estimators of derivatives of regression functions (White and Gallant, 1992) and quantiles of the conditional noise distribution (White, 1992a). Feedforward networks with a single hidden layer using threshold or sigmoid activation functions 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 these results are stronger than the universal approximation theorems that merely show the existence of weights for arbitrarily accurate approximations, without demonstrating that such weights can be obtained by learning.

  1. Pen PC’s: PC’s where one can write on a tablet, and the writing will be recognized and trlated into (ASCII) text.
  2. White goods and toys: As Neural Network chips become available, the possibility of simple cheap systems which have learned to recognize simple entities (e.g. walls looming, or simple commands like Go, or Stop), may lead to their incorporation in toys and washing machines etc. Already the Japanese are using a related technology, fuzzy logic, in this way. There is considerable interest in the combination of fuzzy and neural technologies.

In standard backprop, too low a learning rate makes the network learn very slowly. Too high a learning rate makes the weights and objective function diverge, so there is no learning at all. If the objective function is quadratic, as in linear models, good learning rates can be computed from the Hessian matrix (Bertsekas and Tsitsiklis, 1996). If the objective function has many local and global optima, as in typical feedforward NNs with hidden units, the optimal learning rate often changes dramatically during the training process, since the Hessian also changes dramatically. Trying to train a NN using a constant learning rate is usually a tedious process requiring much trial and error.

It is simply a processor with many inputs and one output….It works in either the Training Mode or Using Mode.  In the training mode, the neuron can be trained to fire (or not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.

The major disadvantage is that they require large diversity of training for working in a real environment. Moreover, they are not strong enough to work in the real world.

How to count layers is a matter of considerable dispute.

  • Some people count layers of units. But of these people, some count the input layer and some don't.
  • Some people count layers of weights. But I have no idea how they count skip-layer connections.

To avoid ambiguity, you should speak of a 2-hidden-layer network, not a 4-layer network (as some would call it) or 3-layer network (as others would call it). And if the connections follow any pattern other than fully connecting each layer to the next and to no others, you should carefully specify the connections.

A vector of values presented at one time to all the input units of a neural network is called a "case", "example", "pattern, "sample", etc. The term "case" will be used in this FAQ because it is widely recognized, unambiguous, and requires less typing than the other terms. A case may include not only input values, but also target values and possibly other information.

A vector of values presented at different times to a single input unit is often called an "input variable" or "feature". To a statistician, it is a "predictor", "regressor", "covariate", "independent variable", "explanatory variable", etc. A vector of target values associated with a given output unit of the network during training will be called a "target variable" in this FAQ. To a statistician, it is usually a "response" or "dependent variable".

Simple difference is that the Artificial Neural Networks can learn by examples contrary to Normal Computers who perform the task on Algorithms. Although, the examples given to Artificial Neural Networks should be carefully chosen. Once properly “taught” Artificial Neural Networks can  do on their own,,,or at least try to imitate..But that makes them so Unpredictable , which is opposite to that of algorithm based computers which we use in our daily life.

Teuvo Kohonen is one of the most famous and prolific researchers in neurocomputing, and he has invented a variety of networks. But many people refer to "Kohonen networks" without specifying which kind of Kohonen network, and this lack of precision can lead to confusion. The phrase "Kohonen network" most often refers to one of the following three types of networks:

  • VQ: Vector Quantization--competitive networks that can be viewed as unsupervised density estimators or autoassociators (Kohonen, 1995/1997; Hecht-Nielsen 1990), closely related to k-me cluster analysis (MacQueen, 1967; Anderberg, 1973). Each competitive unit corresponds to a cluster, the center of which is called a "codebook vector". Kohonen's learning law is an on-line algorithm that finds the codebook vector closest to each training case and moves the "winning" codebook vector closer to the training case.
  • SOM: Self-Organizing Map--competitive networks that provide a "topological" mapping from the input space to the clusters (Kohonen, 1995). The SOM was inspired by the way in which various human sensory impressions are neurologically mapped into the brain such that spatial or other relations among stimuli correspond to spatial relations among the neurons. In a SOM, the neurons (clusters) are organized into a grid--usually two-dimensional, but sometimes one-dimensional or (rarely) three- or more-dimensional. The grid exists in a space that is separate from the input space; any number of inputs may be used as long as the number of inputs is greater than the dimensionality of the grid space. A SOM tries to find clusters such that any two clusters that are close to each other in the grid space have codebook vectors close to each other in the input space. But the converse does not hold: codebook vectors that are close to each other in the input space do not necessarily correspond to clusters that are close to each other in the grid. Another way to look at this is that a SOM tries to embed the grid in the input space such every training case is close to some codebook vector, but the grid is bent or stretched as little as possible. Yet another way to look at it is that a SOM is a (discretely) smooth mapping between regions in the input space and points in the grid space. The best way to undestand this is to look at the pictures in Kohonen (1995) or various other NN textbooks.
  • LVQ: Learning Vector Quantization--competitive networks for supervised classification (Kohonen, 1988, 1995; Ripley, 1996). Each codebook vector is assigned to one of the target classes. Each class may have one or more codebook vectors. A case is classified by finding the nearest codebook vector and assigning the case to the class corresponding to the codebook vector. Hence LVQ is a kind of nearest-neighbor rule.

It is rarely useful to have a NN simply memorize a set of data, since memorization can be done much more efficiently by numerous algorithms for table look-up. Typically, you want the NN to be able to perform accurately on new data, that is, to generalize.

There seems to be no term in the NN literature for the set of all cases that you want to be able to generalize to. Statistici call this set the "population". Tsypkin (1971) called it the "grand truth distribution," but this term has never caught on.

Neither is there a consistent term in the NN literature for the set of cases that are available for training and evaluating an NN. Statistici call this set the "sample". The sample is usually a subset of the population.

(Neurobiologists mean something entirely different by "population," apparently some collection of neurons, but I have never found out the exact meaning. I am going to continue to use "population" in the statistical sense until NN researchers reach a consensus on some other terms for "population" and "sample"; I suspect this will never happen.)

Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:

  • sales forecasting
  • industrial process control
  • customer research
  • data validation
  • risk management
  • target marketing

This is a two paradigm process-

  • Associative Mapping:   Here the network produces a pattern output by working in a pattern on the given input.
  • Regularity Detection:  In this, units learn to respond to particular properties of the input patterns. Whereas in associative mapping the network stores the relationships among patterns, in regularity detection the response of each unit has a particular ‘meaning’. This type of learning mechanism is essential for feature discovery and knowledge representation.

There are many many kinds of NNs by now. Nobody knows exactly how many. New ones (or at least variations of old ones) are invented every week. Below is a collection of some of the most well known methods, not claiming to be complete.

The two main kinds of learning algorithms are supervised and unsupervised.

  • In supervised learning, the correct results (target values, desired outputs) are known and are given to the NN during training so that the NN can adjust its weights to try match its outputs to the target values. After training, the NN is tested by giving it only input values, not target values, and seeing how close it comes to outputting the correct target values.
  • In unsupervised learning, the NN is not provided with the correct results during training. Unsupervised NNs usually perform some kind of data compression, such as dimensionality reduction or clustering.

There are many ways to categorize learning methods. The distinctions are overlapping and can be confusing, and the terminology is used very inconsistently. This wer attempts to impose some order on the chaos, probably in vain.

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

Batch learning proceeds as follows:

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

Incremental learning proceeds as follows:

Initialize the weights. Repeat the following steps: Process one training case. Update the weights.

In the above sketches, the exact meaning of "Process" and "Update" depends on the particular training algorithm and can be quite complicated for methods such as Levenberg-Marquardt Standard backprop (see What is backprop?) is quite simple, though. Batch standard backprop (without momentum) proceeds as follows:

Initialize the weights W. Repeat the following steps: Process all the training data DL to compute the gradient of the average error function AQ(DL,W). Update the weights by subtracting the gradient times the learning rate.

In simple words, a neural network is a connection of many very tiny processing elements called as neurons. There are two types of neural network-

Biological Neural Networks–  These are made of real neurons.Those tiny CPU’s which you have got inside your brain..if u have..Not only brain,,but neurons actually make the whole nervous system.

Artificial Neural Networks– Artificial Neural Networks is an imitation of Biological Neural Networks,,by artificial designing small processing elements, in lieu of using digital computing systems that have only the binary digits. The Artificial Neural Networks are basically designed to make robots give the human quality efficiency to the work.

There is considerable overlap between the fields of neural networks and statistics. Statistics is concerned with data analysis. In neural network terminology, statistical inference me learning to generalize from noisy data. Some neural networks are not concerned with data analysis (e.g., those intended to model biological systems) and therefore have little to do with statistics. Some neural networks do not learn (e.g., Hopfield nets) and therefore have little to do with statistics. Some neural networks can learn successfully only from noise-free data (e.g., ART or the perceptron rule) and therefore would not be considered statistical methods. But most neural networks that can learn to generalize effectively from noisy data are similar or identical to statistical methods. For example:

  • Feedforward nets with no hidden layer (including functional-link neural nets and higher-order neural nets) are basically generalized linear models.
  • Feedforward nets with one hidden layer are closely related to projection pursuit regression.
  • Probabilistic neural nets are identical to kernel discriminant analysis.
  • Kohonen nets for adaptive vector quantization are very similar to k-me cluster analysis.
  • Kohonen self-organizing maps are discrete approximations to principal curves and surfaces.
  • Hebbian learning is closely related to principal component analysis.

The formula for the logistic activation function is often written as:

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

But this formula can produce floating-point overflow in the exponential function if you program it in this simple form. To avoid overflow, you can do this:

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

The constant 45 will work for double precision on all machines that I know of, but there may be some bizarre machines where it will require some adjustment. Other activation functions can be handled similarly.

Combination functions: Each non-input unit in a neural network combines values that are fed into it via synaptic connections from other units, producing a single value called the "net input". There is no standard term in the NN literature for the function that combines values. In this FAQ, it will be called the "combination function". The combination function is a vector-to scalar function. Most NNs use either a linear combination function (as in MLPs) or a Euclidean distance combination function (as in RBF networks). There is a detailed discussion of networks using these two kinds of combination function under "How do MLPs compare with RBFs?"

Activation functions: Most units in neural networks trform their net input by using a scalar-to-scalar function called an "activation function", yielding a value called the unit's "activation". Except possibly for output units, the activation value is fed via synpatic connections to one or more other units. The activation function is sometimes called a "trfer", and activation functions with a bounded range are often called "squashing" functions, such as the commonly used tanh (hyperbolic tangent) and logistic (1/(1+exp(-x)))) functions. If a unit does not trform its net input, it is said to have an "identity" or "linear" activation function. The reason for using non-identity activation functions is explained under "Why use activation functions?"

Error functions: Most methods for training supervised networks require a measure of the discrepancy between the networks output value and the target (desired output) value (even unsupervised networks may require such a measure of discrepancy.

It is weird at the same time amazing to know that we really do not know how we think.  Biologically, neurons in human brain receive signals from host of fine structures called as dendrites.  The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When a neuron receives excitation input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.

"Backprop" is short for "backpropagation of error". The term backpropagation causes much confusion. Strictly speaking, backpropagation refers to the method for computing the gradient of the case-wise error function with respect to the weights for a feedforward network, a straightforward but elegant application of the chain rule of elementary calculus (Werbos 1974/1994). By extension, backpropagation or backprop refers to a training method that uses backpropagation to compute the gradient. By further extension, a backprop network is a feedforward network trained by backpropagation.

Yes of course…

  1. Electronic noses: ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odors in the remote surgical environment. These identified odors would then be electronically trmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
  2. Instant Physician: An application developed in the mid-1980s called the “instant physician” trained an auto-associative memory neural network to store a large number of medical records, each of which includes information on symptoms, diagnosis, and treatment for a particular case. After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the “best” diagnosis and treatment.

Training a neural network is, in most cases, an exercise in numerical optimization of a usually nonlinear objective function ("objective function" me whatever function you are trying to optimize and is a slightly more general term than "error function" in that it may include other quantities such as penalties for weight decay;

Methods of nonlinear optimization have been studied for hundreds of years, and there is a huge literature on the subject in fields such as numerical analysis, 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 good elementary discussion of conjugate gradient and Levenberg-Marquardt algorithms in the context of NNs.

Mainly, Artificial Neural Networks OR Artificial Intelligence is designed to give robots human quality thinking. So that machines can decide “What if” and ”What if not” with precision. Some of the other advantages are:-

  • Adaptive learning: Ability to learn how to do tasks based on the data given for training or initial experience.
  • Self-Organization: An Artificial Neural Networks can create its own organization or representation of the information it receives during learning time.
  • Real Time Operation: Artificial Neural Networks computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
  • Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Numerical condition is one of the most fundamental and important concepts in numerical analysis. Numerical condition affects the speed and accuracy of most numerical algorithms. Numerical condition is especially important in the study of neural networks because ill-conditioning is a common cause of slow and inaccurate results from backprop-type algorithms.