Neural Networks Interview Questions and Answers 2024

One of the simplest definition of a Neural Network, more properly referred to as an ‘artificial’ neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as:

“A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs”.

An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells. Neural networks are parallel and distributed information processing systems that are inspired and derived from biological learning systems such as human brains.

The architecture of neural networks consists of a network of nonlinear information processing elements that are normally arranged in layers and executed in parallel. This layered arrangement for the network is referred to as the topology of a neural network. These nonlinear information processing elements in the network are defined as neurons, and the interconnections between these neurons in the network are called synapse or weights. A learning algorithm must be used to train a neuralnetwork so that it can process information in a useful and meaningful way.

There are different types of Neural Networks:

ANN-Autoencoder Neural Networks

Biological Neural Networks

MNN-Modular Neural Networks

PNN-Probabilistic Neural Networks

PNN-Physical Neural Networks

SNN-Spiking Neural Networks

SNN-Stochastic Neural Networks

CNN-Convolutional Neural Networks

CNN-Cascading Neural Networks

CCN-Cascade Correlation Networks

DNN-Dynamic Neural Networks

HNN-Hopfield Neural Networks

FNN-Feedforward Neural Networks

TDNN-Time delay Neural Networks

RBFNN-Radial basis function Neural Network

KSONN -Kononen self organizing Neural Networks

RNN- Recurrent Neural Networks

CPNN-Compositional Pattern Producing Neural Networks

LVQ-Learning Vector Quantization Networks

FLN-Functional Link Networks

GCN-Gram-Chalier Networks

HN-Hybrid Networks

HN-Hebb Networks

KN-Kohonen Networks

AN-Adaline Networks

HaN or HN-Hetero-associative Networks

HAM-Holographic Associative Memory

GRNN-General Regression Neural Networks

ITNN -Instantaneously trained Neural Networks

MN-Memory Networks

The Modular Neural Network (MNN) is a neural network that has two main branches. During the training process, branches compete against each other, resulting in a system that is capable of better generalization.

A neural network usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information — analogous to optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it, rather than from the raw input — in the same way neurons further from the optic nerve receive signals from those closer to it. The last tier produces the output of the system.

Each processing node has its own small sphere of knowledge, including what it has seen and any rules it was originally programmed with or developed for itself. The tiers are highly interconnected, which means each node in tier n will be connected to many nodes in tier n-1 — its inputs — and in tier n+1, which provides input for those nodes. There may be one or multiple nodes in the output layer, from which the answer it produces can be read.

Neural networks are notable for being adaptive, which means they modify themselves as they learn from initial training and subsequent runs provide more information about the world. The most basic learning model is centered on weighting the input streams, which is how each node weights the importance of input from each of its predecessors. Inputs that contribute to getting right answers are weighted higher. ( Source: TechTarget)

There are several ways artificial neural networks can be deployed including to classify information, predict outcomes and cluster data. As the networks process and learn from data they can classify a given data set into a predefined class, it can be trained to predict outputs that are expected from a given input and can identify a special feature of data to then classify the data by that special feature.

Google uses a 30-layered neural network to power Google Photos as well as to power its “watch next” recommendations for YouTube videos. Facebook uses artificial neural networks for its Deep Face algorithm, which can recognize specific faces with 97% accuracy. It’s also an ANN that powers Skype’s ability to do translations in real-time.

Computers have the ability to understand the world around them in a very human-like manner thanks to the power of artificial neural networks.

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.

There is considerable overlap between the fields of neural networks and statistics. Statistics is concerned with data analysis. In neural network terminology, statistical inference means 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-means cluster analysis.
• Kohonen self-organizing maps are discrete approximations to principal curves and surfaces.

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.

They can perform tasks that are easy for a human but difficult for a machine −

Aerospace/Defence: Autopilot aircrafts, aircraft fault detection.

Automotive Industry: Automobile guidance systems.

Military Systems: Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.

Electronics Applications: Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.

Medical Applications: Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.

Industrial: Manufacturing process control, product design and analysis, quality inspection systems, welding quality analysis, paper quality prediction, chemical product design analysis, dynamic modelling of chemical process systems, machine maintenance analysis, project bidding, planning, and management.

Financial/banking: Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.

Speech: Speech recognition, speech classification, text to speech conversion.

Telecommunications: Image and data compression, automated information services, real-time spoken language translation.

Transportation: Truck Brake system diagnosis, vehicle scheduling, routing systems.

Software or IT: Pattern Recognition in facial recognition, optical character recognition, etc.

Time Series Prediction: ANNs are used to make predictions on stocks and natural calamities.

Signal Processing: Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.

Control: ANNs are often used to make steering decisions of physical vehicles.

Anomaly Detection:  As ANNs are expert at recognizing patterns, they can also be trained to generate an output when something unusual occurs that misfits the pattern.

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.

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”.

NeuroSolutions includes two wizards to create neural networks for you based on your data and specifications. One of these wizards, the NeuralBuilder, centers the design specifications on the specific neural network architecture you wish to have built. Some of the most common neural network architectures include:

MLP -Multilayer Perceptron

GFNN-Generalized Feedforward Neural Network

RN-Recurrent Network

MNN-Modular Neural Network

JNN-Jordan/Elman Neural Network

PNN-Probabilistic Neural Network

PCA-Principal Component Analysis

RBF-Radial Basis Function

GRNN-General Regression Neural Network

SOM-Self-Organizing Map

TLRN-Time-Lag Recurrent Network

CANFIS Network (Fuzzy Logic)

     SVM-Support Vector Machine

Unsupervised Learning (i.e. without Guide or Lecture):

Feedback Nets:

AG-Additive Grossberg

SG-Shunting Grossberg

BART1-Binary Adaptive Resonance Theory

ART2,ART2a-Analog Adaptive Resonance Theory

DH-Discrete Hopfield

CH-Continuous Hopfield

DBAM-Discrete Bidirectional Associative Memory

TAM-Temporal Associative Memory

ABAN-Adaptive Bidirectional Associative Memory

KSOM-Kohonen Self-organizing Map

KTPM-Kohonen Topology-preserving Map

Feedforward-only Nets:

LM-Learning Matrix (LM)

DRL-Driver-Reinforcement Learning

LAM-Linear Associative Memory

OLAM-Optimal Linear Associative Memory

SDAM-Sparse Distributed Associative Memory

FAM-Fuzzy Associative Memory

CPN-Counterprogation

Supervised Learning(i.e. with “Guide or Lecture”):

Feedback Nets:

BSB-Brain-State-in-a-Box

FCM-Fuzzy Congitive Map

BM-Boltzmann Machine

MFT-Mean Field Annealing

RCC-Recurrent Cascade Correlation

LVQ-Learning Vector Quantization

Feedforward-only Nets:

Perceptron

ANN-Adaline,MNN-Madaline

BP-Backpropagation

CM-Cauchy Machine

AHC-Adaptive Heuristic Critic

TDNN-Time Delay Neural Network

ARP-Associative Reward Penalty

AMF-Avalanche Matched Filter

Perc-Backpercolation

AM-Artmap

ALN-Adaptive Logic Network

CCNN-Cascade Correlation