Advantages and Disadvantages of ANN
The advantages are listed below
1. A neural network can perform tasks that a linear program can not.2. When an element of the neural network fails, its parallel nature can continue without any problem.
3. A neural network learns, and reprogramming is not necessary.
4. It can be implemented in any application.
5. It can be performed without any problem.
The disadvantages are described below
1. The neural network needs training to operate.2. The architecture of a neural network is different from the architecture of microprocessors. Therefore, emulation is necessary.
3. Requires high processing time for large neural networks.
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What are the parts of Neurons and their Functions?
The typical nerve cell of the human brain comprises 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.
Differences Between Brain and Computer
ANN vs BNN: Key Distinctions
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

Relationship Between ANN and BNN
Neural Networks resemble the human brain in the following two ways 

A neural network acquires knowledge through learning.

A neural network's knowledge is stored within interneuron connection strengths known as synaptic weights.
Von Neumann architecturebased computing

AnnBased 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 ANN 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.

Every input is multiplied by its specific weights, which serve as crucial information for the neural network to solve problems. These weights essentially represent the strength of the connections between neurons within 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.
Types of Neural Networks
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
Neural Network is having two input units and one output unit with no hidden layers. These are also known as ‘singlelayer perceptrons.
Radial Basis Function
These networks are similar to the feedforward Neural Network, except the radial basis function is used as these neurons' activation function.
Multilayer Perceptron
Unlike singlelayer perceptron, these networks use more than one hidden layer of neurons. These are also known as Deep Feedforward Neural Networks.
Recurrent
Type of Neural Network in which hidden layer neurons have selfconnections. It possesses memory. At any instance, the hidden layer neuron receives activation from the lower layer and its previous activation value.
Long ShortTerm Memory Neural Network (LSTM)
The type of Neural Network in which memory cell is incorporated into hidden layer neurons is called an LSTM network.
Hopfield Network
A fully interconnected network of neurons in which each neuron is connected to every other neuron. The network is trained with input patterns by setting a value of neurons to the desired pattern. Then its weights are computed. The weights are not changed. Once trained for one or more patterns, the network will converge to the learned patterns. It is different from other Neural Networks.
Boltzmann Machine Neural Network
These networks are similar to the Hopfield network, except some neurons are input, while others are hidden in nature. The weights are initialized randomly and learn through the backpropagation algorithm.
Convolutional Neural Network
Get a complete overview of it through our blog Log Analytics with Machine Learning and Deep Learning.
Modular Neural Network
It is the combined structure of different types of it like multilayer perceptron, Hopfield Networks, Recurrent Neural Networks, etc., which are incorporated as a single module into the network to perform independent subtasks of whole complete.
Physical Neural Network
In this type of Artificial Neural Network, electrically adjustable resistance material is used to emulate synapses instead of software simulations performed in the neural network.
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Hardware Architecture for Neural Networks
Two types of methods are used to implement hardware for it.

Software simulation in conventional computer

A special hardware solution for decreasing execution time.
When Neural Networks are used with fewer processing units and weights, software simulation is performed on the computer directly. E.g., voice recognition, etc. When Neural Network Algorithms developed to the point where useful things can be done with 1000's neurons and 10000's of synapses, highperformance Neural network hardware will become essential for practical operation.
E.g., GPU (Graphical processing unit) in the case of Deep Learning algorithms in object recognition, image classification, etc. The implementation's performance is measured by connection per the second number (cps), i.e., the number of the data chunk is transported through the neural network's edges. While the performance of the learning algorithm is measured in the connection updates per second (cups).
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
i. Gradient Descent Algorithm
This is the simplest training algorithm used in the case of a supervised training model. In case the actual output is different from the target output, the difference or error is find out. The gradient descent algorithm changes the weights of the network in such a manner as to minimize this mistake.
ii. Back Propagation Algorithm
It is an extension of the gradientbased delta learning rule. Here, after finding an error (the difference between desired and target), the error is propagated backward from the output layer to the input layer via the hidden layer. It is used in the case of Multilayer Neural Networks.
Learning Data Sets

Training Data Set: A set of examples used for learning is to fit the parameters [i.e., weights] of the network. One approach comprises one full training cycle on the training set.

Validation Set Approach: A set of examples used to tune the parameters [i.e., architecture] of the network. For example, to choose the number of hidden units in a Neural Network.

Making Test Set: A set of examples is used only to assess the performance [generalization] of a fully specified network or apply successfully to predict output whose input is known.
Five Algorithms to Train a Neural Network

Hebbian Learning Rule

SelfOrganizing Kohonen Rule

Hopfield Network Law

LMS algorithm (Least Mean Square)

Competitive Learning
Architecture of Neural Networks
A typical Neural Network contains many artificial neurons called units arranged in layers. A typical Artificial Neural Network comprises different layers 
Input layer
It contains those units (Artificial Neurons) that receive input from the outside world on which the network will learn, recognize, 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 the input and output layers. The hidden layer's job is to transform the input into something the output unit can use.

Connect Neural Networks, which means say, each hidden neuron links completely to every neuron in its previous layer(input) and the next layer (output) layer.
Learning Techniques
Here is a list of Learning Techniques

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Offline Learning

Online Learning
Let's Discuss each one of them in length

Supervised Learning
In this learning, the training data is input to the network, and the desired output is known weights are adjusted until production yields the desired value.

Unsupervised Learning
Use the input data to train the network whose output is known. The network classifies the input data and adjusts the weight by feature extraction in input data.

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 and threshold adjustments are made only after the training set is shown to the network. It is also called Batch Learning.

Online Learning
The adjustment of the weight and threshold is made after presenting each training sample to the network.
Learning and Development
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.
We want to classify input patterns into either pattern ‘I’ & ‘O.' The following are the steps performed:

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 formulas:

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 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]
So, these weights correspond to the learning ability of the network to classify the input patterns successfully.
Use Cases of ANN
There are four broad use cases of Neural Networks

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 expected outputs 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. The 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, clustering, etc.
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.
So, the recognition problem here is essentially a classification or categorized task. The design of pattern recognition systems usually involves the following three aspects

Data acquisition and preprocessing

Data representation

Decision Making
Approaches For Pattern Recognition

Template Matching

Statistical

Syntactic Matching
Following Neural Network architectures used for Pattern Recognition 
 Multilayer Perceptron
 Kohonen SOM (SelfOrganizing 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
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 for use in the following applications 

Automotive engineering

Applicant screening of jobs

Control of the crane

Monitoring of glaucoma
In a hybrid (neurofuzzy) model, Neural Networks Learning Algorithms are fused with the fuzzy reasoning of fuzzy logic. It determines the values of parameters, while ifthen rules are controlled by fuzzy logic.
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
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 be analysed and faces identified earlier than they can be recognized.
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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 relies 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: The 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 the error and the input.

Correlation learning rule: It is similar to supervised learning.
How XenonStack Can Assist You
XenonStack can help you develop and deploy your model solutions based on Neural Networks. Whatever kind of problem you face  Prediction, Classification, or Pattern Recognition  XenonStack has a solution for you.
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