Subscription
Thanks for submitting the form.
What is an Artificial Neural Network?
These are computational models and inspire by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using it. They are the biologically inspired simulations performed on the computer to perform certain specific tasks like  Clustering
 Classification
 Pattern Recognition
What is a Neural Network?
The term ‘Neural’ has origin from the human (animal) nervous system’s basic functional unit ‘neuron’ or nerve cells present in the brain and other parts of the human (animal) body. A neural network is a group of algorithms that certify the underlying relationship in a set of data similar to the human brain. The neural network helps to change the input so that the network gives the best result without redesigning the output procedure. You can also learn more about ONNX in this insight.
What are the advantages and disadvantages of it?
The advantages of are listed below:
 A neural network can perform tasks that a linear program can not.
 When an element of the neural network fails, its parallel nature can continue without any problem.
 A neural network learns and reprogramming is not necessary.
 It can be implemented in any application.
 It can be performed without any problem.
The disadvantages of are described below:
 The neural network needs training to operate.
 The architecture of a neural network is different from the architecture of microprocessors. Therefore, emulation is necessary.
 Requires high processing time for large neural networks.
A combination of neurons whose performance vector signifies the creation of real instance parameters of a particular type of an object or it's part.Click to explore about our, Capsule Networks Benefits
What are the parts of Neuron and their Functions?
The typical nerve cell of the human brain comprises of four parts:
Function of Dendrite
It receives signals from other neurons.
Soma (cell body)
It sums all the incoming signals to generate input.Axon Structure
When the sum reaches a threshold value, the neuron fires, and the signal travels down the axon to the other neurons.
Synapses Working
The point of interconnection of one neuron with other neurons. The amount of signal transmitted depends upon the strength (synaptic weights) of the connections. The connections can be inhibitory (decreasing strength) or excitatory (increasing strength) in nature. So, a neural network, in general, has a connected network of billions of neurons with a trillion of interconnections between them.
What is the difference between brain and computer?
What is the difference between Artificial Neural Networks (ANN) VS Biological Neural Networks (BNN)?
Characteristics  Artificial Neural Network (ANN)  Biological(Real) Neural Network (BNN) 
Speed  Faster in processing information. Response time is in nanoseconds.  Slower in processing information. The response time is in milliseconds. 
Processing  Serial processing.  Massively parallel processing. 
Size & Complexity  Less size & complexity. It does not perform complex pattern recognition tasks.  A highly complex and dense network of interconnected neurons containing neurons of the order of 1011 with 1015 of interconnections.<strong 
Storage  Information storage is replaceable means replacing new data with an old one.  A highly complex and dense network of interconnected neurons containing neurons of the order of 1011 with 1015 of interconnections. 
Fault tolerance  Fault intolerant. Corrupt information cannot retrieve in case of failure of the system.  Information storage is adaptable means new information is added by adjusting the interconnection strengths without destroying old information. 
Control Mechanism  There is a control unit for controlling computing activities  No specific control mechanism external to the computing task 
Artificial Neural Networks with Biological Neural Network
Neural Networks resemble the human brain in the following two ways  A neural network acquires knowledge through learning.
 A neural network's knowledge is a store within interneuron connection strengths known as synaptic weights.
Von Neumann Architecture Based Computing 
Ann Based Computing 
Serial processing  processing instruction and problem rule one at the time (sequential)  Parallel processing  several processors perform simultaneously (multitasking) 
Function logically with a set of if & else rules  rulebased approach  Function by learning pattern from a given input (image, text or video, etc.) 
Programmable by higherlevel languages such as C, Java, C++, etc.  ANN is, in essence, the program itself. 
Requires either big or errorprone parallel processors  Use of applicationspecific multichips. 
Artificial Neural Network (ANN) Vs Biological Neural Network (BNN)
 The Biological Neural Network's dendrites are analogous to the weighted inputs based on their synaptic interconnection in it.
 The cell body is comparable to the artificial neuron unit in it, comprising summation and threshold unit.
 Axon carries output that is analogous to the output unit in the case of it. So, it is model using the working of basic biological neurons.
How does it works?
 It can be viewed as weighted directed graphs in which artificial neurons are nodes, and directed edges with weights are connections between neuron outputs and neuron inputs.
 The Artificial Neural Network receives information from the external world in pattern and image in vector form. These inputs are designated by the notation x(n) for n number of inputs.
 Each input is multiplied by its corresponding weights. Weights are the information used by the neural network to solve a problem. Typically weight represents the strength of the interconnection between neurons inside the Neural Network.
 The weighted inputs are all summed up inside the computing unit (artificial neuron). In case the weighted sum is zero, bias is added to make the output not zero or to scale up the system response. Bias has the weight and input always equal to ‘1'.
 The sum corresponds to any numerical value ranging from 0 to infinity. To limit the response to arrive at the desired value, the threshold value is set up. For this, the sum is forward through an activation function.
 The activation function is set to the transfer function to get the desired output. There are linear as well as the nonlinear activation function.
What are the commonly used activation functions?
Some of the commonly used activation function is  binary, sigmoidal (linear) and tan hyperbolic sigmoidal functions(nonlinear). Binary  The output has only two values, either 0 and 1. For this, the threshold value is set up. If the net weighted input is greater than 1, the output is assumed as one otherwise zero.
 Sigmoidal Hyperbolic  This function has an ‘S’ shaped curve. Here the tan hyperbolic function is used to approximate output from net input. The function is defined as  f (x) = (1/1+ exp(????x)) where ????  steepness parameter.
What are its various types ?
Parameter 
Types 
Description 
Based on the connection pattern  FeedForward, Recurrent  Feedforward  In which graphs have no loops. Recurrent  Loops occur because of feedback. 
Based on the number of hidden layers  Singlelayer, MultiLayer  Single Layer  Having one secret layer. E.g., Single Perceptron Multilayer  Having multiple secret layers. Multilayer Perceptron 
Based on the nature of weights  Fixed, Adaptive  Fixed  Weights are a fixed priority and not changed at all. Adaptive  Updates the weights and changes during training. 
Based on the Memory unit  Static, Dynamic  Static  Memoryless unit. The current output depends on the current input. E.g., Feedforward network. Dynamic  Memory unit  The output depends upon the current input as well as the current output. E.g., Recurrent Neural Network 
Neural Network Architecture Types
 Perceptron Model in Neural Networks
 Radial Basis Function Neural Network
 Multilayer Perceptron Neural Network
 Recurrent Neural Network
 Long ShortTerm Memory Neural Network (LSTM)
 Hopfield Network
 Boltzmann Machine Neural Network
 Convolutional Neural Network
 Modular Neural Network
 Physical Neural Network

Perceptron Model

Radial Basis Function

Multilayer Perceptron

Recurrent

Long ShortTerm Memory Neural Network (LSTM)

Hopfield Network

Boltzmann Machine Neural Network

Convolutional Neural Network

Modular Neural Network

Physical Neural Network
Artificial Intelligence collects and analyze data using smart sensors or machine learning algorithms and automatically route service requests to reduce the human workload. Click to explore about our, Artificial Intelligence Applications
Hardware Architecture for Neural Networks
Two types of methods are used for implementing hardware for it. Software simulation in conventional computer
 A special hardware solution for decreasing execution time.
Learning Techniques
The neural network learns by adjusting its weights and bias (threshold) iteratively to yield the desired output. These are also called free parameters. For learning to take place, the Neural Network is trained first. The training is performed using a defined set of rules, also known as the learning algorithm.Training Algorithms
 Gradient Descent Algorithm
 Back Propagation Algorithm
Learning Data Sets

Training Data Set

Validation Set Approach

Making Test Set
What are the Five Algorithms to Train a Neural Network?
 Hebbian Learning Rule
 Self  Organizing Kohonen Rule
 Hopfield Network Law
 LMS algorithm (Least Mean Square)
 Competitive Learning
What is the architecture of it?
A typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers. In typical Artificial Neural Network comprises different layers  Input layer  It contains those units (Artificial Neurons) which receive input from the outside world on which the network will learn, recognize about, or otherwise process.
 Output layer  It contains units that respond to the information about how it learn any task.
 Hidden layer  These units are in between input and output layers. The hidden layer's job is to transform the input into something that the output unit can use somehow.
What are the Learning Techniques in Neural Networks?
Here is a list of Learning Techniques
 Supervised Learning
 Unsupervised Learning
 Reinforcement Learning
 Offline Learning
 Online Learning
 Supervised Learning
 Unsupervised Learning
 Reinforcement Learning
Here, the output value is unknown, but the network provides feedback on whether the output is right or wrong. It is SemiSupervised Learning.
 Offline Learning
The weight vector adjustment and threshold adjustment are made only after the training set is shown to the network. It is also called Batch Learning.
 Online Learning
Learning and Development in Neural Networks
Learning occurs when the weights inside the network get updated after many iterations. For example  Suppose we have inputs in the form of patterns for two different classes of patterns  I & 0 as shown and b bias and y as the desired output.
Pattern 
y 
x1 
x2 
x3 
x4 
x5 
x6 
x7 
x8 
x9 
b 
I 
1  1  1  1  1  1  1  1  1  1  1 
O 
1  1  1  1  1  1  1  1  1  1  1 
 Nine inputs from x1  x9 and bias b (input having weight value 1) are fed to the network for the first pattern.
 Initially, weights are initialized to zero.
 Then weights are updated for each neuron using the formulae: Δ wi = xi y for i = 1 to 9 (Hebb’s Rule)
 Finally, new weights are found using the formulae:
 wi(new) = wi(old) + Δwi
 Wi(new) = [111111 1111]
 The second pattern is input to the network. This time, weights are not initialized to zero. The initial weights used here are the final weights obtained after presenting the first pattern. By doing so, the network.
 The steps from 1  4 are repeated for second inputs.
 The new weights are Wi(new) = [0 0 0 2 2 2 000]
What are the usecases of it?
There are four broad usecases of Neural Network
 Classification Neural Network
 Prediction Neural Network
 Clustering Neural Network
 Association Neural Network
Classification Neural Network
A Neural Network can be trained to classify a given pattern or dataset into a predefined class. It uses Feedforward Networks.
Prediction Neural Network
A Neural Network can be trained to produce outputs that are expected from a given input. E.g.,  Stock market prediction.
Clustering Neural Network
The Neural network can identify a unique feature of the data and classify them into different categories without any prior knowledge of the data. Following networks are used for clustering 
 Competitive networks
 Adaptive Resonance Theory Networks
 Kohonen SelfOrganizing Maps.
Association Neural Network
Train the Neural Network to remember the particular pattern. When the noise pattern is presented to the network, the network associates it with the memory's closest one or discards it. E.g., Hopfield Networks, which performs recognition, classification, and clustering, etc.
What are the applications of neural networks?
 Neural Network for Machine Learning
 Face Recognition using it
 NeuroFuzzy Model and its applications
 Neural Networks for dataintensive applications
Neural Networks for Pattern Recognition
Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns' categories. Some examples of the pattern are  fingerprint images, a handwritten word, a human face, or a speech signal. Given an input pattern, its recognition involves the following task  Supervised classification  Given the input pattern is known as the member of a predefined class.
 Unsupervised classification  Assign pattern is to a hitherto unknown class.
 Data acquisition and preprocessing
 Data representation
 Decision Making
Approaches For Pattern Recognition
 Template Matching
 Statistical
 Syntactic Matching
 Multilayer Perceptron
 Kohonen SOM (Self Organizing Map)
 Radial Basis Function Network (RBF)
A division of unsupervised learning which makes it more handful because it can also handle unsupervised learning which is itself a big plus. Click to explore about our, Generative Adversarial Networks Applications
NeuroFuzzy Model and its applications
What is Fuzzy logic?
Fuzzy logic refers to the logic developed to express the degree of truthiness by assigning values between 0 and 1, unlike traditional boolean logic representing 0 and 1.
What is Fuzzy logic role in Neural networks?
Fuzzy logic and it have one thing in common. They can be used to solve pattern recognition problems and others that do not involve any mathematical model.
What are the applications of NeuroFuzzy Model?
Systems combining both fuzzy logic and neural networks are neurofuzzy systems. These systems (Hybrid) can combine the advantages of both it and fuzzy logic to perform in a better way. Fuzzy logic and it have been integrated to use in the following applications 
 Automotive engineering
 Applicant screening of jobs
 Control of crane
 Monitoring of glaucoma
Neural Network for Machine Learning
 Multilayer Perceptron (supervised classification)
 Back Propagation Network (supervised classification)
 Hopfield Network (for pattern association)
 Deep Neural Networks (unsupervised clustering)
Neural Networks for dataintensive applications
It have been successfully applied to the broad spectrum of dataintensive applications, such as:Application  Architecture / Algorithm  Activation Function 
Process modeling and control  Radial Basis Network  Radial Basis 
Machine Diagnostics  Multilayer Perceptron  Tan Sigmoid Function 
Portfolio Management  Classification Supervised Algorithm  Tan Sigmoid Function 
Target Recognition  Modular Neural Network  Tan Sigmoid Function 
Medical Diagnosis  Multilayer Perceptron  Tan Sigmoid Function 
Credit Rating  Logistic Discriminant Analysis with ANN, Support Vector Machine  Logistic function 
Targeted Marketing  Back Propagation Algorithm  Logistic function 
Voice recognition  Multilayer Perceptron, Deep Neural Networks( Convolutional Neural Networks)  Logistic function 
Financial Forecasting  Backpropagation Algorithm  Logistic function 
Intelligent searching  Deep Neural Network  Logistic function 
Fraud detection  Gradient  Descent Algorithm and Least Mean Square (LMS) algorithm.  Logistic function 
Face Recognition using Artificial Neural Networks
Face recognition entails comparing an image with a database of saved faces to identify the person in that input picture. It is a mechanism that involves dividing images into two parts; one containing targets (faces) and one providing the background. The associated assignment of face detection has direct relevance to the fact that images need to analyze and faces identified earlier than they can be recognized.
Face Recognition uses computer algorithms to find specific details about a person's face. Click to explore about our, Face Recognition with Deep Learning
What is learning rule in neural network?
The learning rule is a type of mathematical logic. It encourages to gain from the present conditions and upgrade its efficiency and performance. The learning procedure of the brain modifies its neural structure. The expanding or diminishing quality of its synaptic associations rely upon their activity. Learning rules in the Neural network:
 Hebbian learning rule: It determines how to customize the weights of nodes of a system.
 Perceptron learning rule: Network starts its learning by assigning a random value to each load.
 Delta learning rule: Modification in a node's sympatric weight is equal to the multiplication of error and the input.
 Correlation learning rule: It is similar to supervised learning.
How can XenonStack help you?
Fraud Detection & Prevention Services
XenonStack Fraud Detection Services offers realtime fraud analysis to increase profitability. Data Mining is beneficial to detect fraud quickly and search for spot patterns and detect fraudulent transactions. Tools for Data Mining like Machine Learning, Cluster Analysis are beneficial to generate Predictive Models to prevent fraud losses.Data Modeling Services
XenonStack offers Data Modelling using Neural Networks, Machine Learning, and Deep Learning. Data Modelling services help Enterprises to create a conceptual model based on the analysis of data objects. Deploy your Data Models on leading Cloud Service Providers like Google Cloud, Microsoft Azure, AWS, or on the container environment  Kubernetes & Docker. Read more about Open Neural Network Exchange Advantages
 Explore here about Machine Learning Model Visualization