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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 -
- 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.
When the sum reaches a threshold value, the neuron fires, and the signal travels down the axon to the other neurons.
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 NetworkNeural 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 inter-neuron 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 - rule-based approach||Function by learning pattern from a given input (image, text or video, etc.)|
|Programmable by higher-level languages such as C, Java, C++, etc.||ANN is, in essence, the program itself.|
|Requires either big or error-prone parallel processors||Use of application-specific multi-chips.|
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 ?
|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||Single-layer, Multi-Layer||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 Short-Term Memory Neural Network (LSTM)
- Hopfield Network
- Boltzmann Machine Neural Network
- Convolutional Neural Network
- Modular Neural Network
- Physical Neural Network
Radial Basis Function
Long Short-Term Memory Neural Network (LSTM)
Boltzmann Machine Neural Network
Convolutional Neural Network
Modular Neural Network
Physical Neural Network
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Hardware Architecture for Neural NetworksTwo types of methods are used for implementing hardware for it.
- Software simulation in conventional computer
- A special hardware solution for decreasing execution time.
Learning TechniquesThe 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.
- 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 Semi-Supervised 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 NetworksLearning 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.
- 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) = [111-11-1 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 use-cases of it?
There are four broad use-cases 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 Self-Organizing 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
- Neuro-Fuzzy Model and its applications
- Neural Networks for data-intensive applications
Neural Networks for Pattern RecognitionPattern 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
- 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
Neuro-Fuzzy 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 Neuro-Fuzzy Model?
Systems combining both fuzzy logic and neural networks are neuro-fuzzy 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 data-intensive applicationsIt have been successfully applied to the broad spectrum of data-intensive 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.