Artificial Neural Networks: A Comprehensive 10 Step Guide
Introduction
An Artificial Neural Network (ANN) in computing is a lot like the neurons in the human brain. Neural Network in Artificial Intelligence is a complex system of hardware and software that forms many Neural Networks.
Artificial Neural Network application is commercially used on a large scale. It solves problems centered around simple signal processing or complex pattern recognition. An introduction to Artificial Neural Network will help you understand critical components and build clarity on the following:
- What Is An Artificial Neural Network?
- What Is The Purpose Of An Artificial Neural Network?
- What Are The Components Of Artificial Neural Network?
- What Are The Characteristics Of An Artificial Neural Network?
- Functions Of ANN
- What Is The Use Of Artificial Neural Network?
- What Are The Types Of Artificial Neural Network?
- What Is An Artificial Neural Network Algorithm?
- Artificial Neural Network Examples
- What Are The Advantages And Disadvantages Of Artificial Neural Network?
1. What Is An Artificial Neural Network?
You can think of an Artificial Neural Network in a similar way to how the neurons in the human brain work. In Artificial Intelligence, it is the pillar on which AI technology rests.
Artificial Neural Network Definition: A subfield of Artificial Intelligence that is inspired by biology and modeled after the human brain.
What Is A Neural Network In Artificial Intelligence?
Neural Networks, a part of AI, is a technology that replicates the human brain’s functions. Akin to neurons in the human brain, Artificial Neural Networks help complex information processing in many complicated ways.
2. What Is The Purpose Of An Artificial Neural Network?
The goal of an ANN is to provide data transformations to influence a decision. This data transformation is an outcome of a hierarchical data representation. This hierarchical structure is important in classifying data that originates from different concepts and classes.
An Artificial Neural Network can be used for any procedure that requires prediction as its outcome.
3. What Are The Components Of Artificial Neural Network?
When studying the basics of Artificial Neural Network, the best approach is by breaking down the components. There are three main components of an Artificial Neural Network:
- Neurons: This is the most basic section of the Neural Network. It receives information from an external source or from other nodes.
- Weights: Each node in a Neural Network is connected to the other. Every such connection has a unique weight assigned to it, calculated based on its relative importance to other input layers.
- Input layer: This is the outermost layer of the Artificial Neural Network.
4. What Are The Characteristics Of An Artificial Neural Network?
The working of Artificial Neural Networks is based on some standard characteristics, described below:
- Any ANN will possess a huge number of neurons to help process information.
- Each neuron in an ANN will have an assigned weighted connection to one another.
- The interneuron connections provide distributed data representation.
- Finally, with the help of the implementation of a learning process, new knowledge is acquired.
5. Functions Of ANN
Neural Networks are especially useful in retaining valuable information and making sense of large data sets. They perform certain important tasks that make them indispensable to deep tech.
- Classification
The main task performed by Neural Networks is to classify patterns or datasets into patterns that are predefined.
- Prediction
With a given input, Neural Networks can produce the expected output with the utmost accuracy.
- Clustering
This is an incredibly unique task that an Artificial Neural Network can perform. By identifying unique features of data without any prior knowledge, neurons can group data using this technique.
- Associating
Neural Networks can be trained to remember and retain data patterns.
6. What Is The Use Of Artificial Neural Network?
The Artificial Neural Network characteristics and the Artificial Neural Network techniques make them suitable for various practical applications. Artificial Neural Network classification is a popular technique used to collect data sets of images. Further, the ANN is trained to process these images. Subsequently, if you add a new image, the ANN can statistically give you the accuracy used to match the original set of images.
Artificial Neural Network In Machine Learning
In Machine Learning, you can use an existing ANN architecture. Or, you can design your own as per your discretion. Your application needs will guide your decision for the same. Essentially, there is no standard guideline that tells you to use a specific architecture.
Real-World Uses Of ANN:
Artificial Neural Network is abundantly used across industries and services. Some of the more common industries that widely use ANN are:
- Aerospace
- Automotive
- Engineering (Deep Learning)
- Manufacturing
- Electronics
- Mechanics
- Robotics
- Telecommunications
- Banking
- Analytics
- Defense
- Education
- Finance
- Medicine
- Transportation and Logistics
7. What Are The Types Of Artificial Neural Network?
You will find various types of ANN in Artificial Intelligence. Depending on the properties of Artificial Neural Networks, you can determine their application and use. Here are the most common types of ANN used in ML.
- Feedforward Neural Network
This is the simplest form of Neural Network. Here, the data passes through various input nodes until it reaches the output node. This implies that the movement of data is unidirectional. This type of movement is called a front propagated wave, achieved by using a classifying activation function.
- Radial Basis Function Neural Network
This is a two-layered Neural Network where the inner layer consists of features that combine with the radial basis function. The latter considers the distance of a particular point in relation to the center. You will find wide usage of this type of Neural Network in big and complex power restoration systems.
- Multilayer Perceptron
An ANN with three of more layers constitutes this type. It is extensively used in data classification, where data cannot be linearly separated. Thus, it uses a non-linear, hyperbolic activation function.
- Convolutional Neural Network (CNN)
This is a variant of the multilayer perceptron that contains many interconnected or pooled convolutional layers. The uniqueness of CNN is that it uses a convolutional operation on the result before passing it on to the next layer. This results in a much deeper network but with fewer parameters.
- Recurrent Neural Network (RNN)
To help the prediction of outcome, RNN saves the output of a particular layer and feeds it back to the input. Here, the nodes act more like memory cells while they compute and execute different operations.
- Modular Neural Network (MNN)
In an MNN, different networks act independently without interacting with one another. Each of them can perform individual sub-tasks that contribute to the final output.
- Sequence-to-Sequence Models
Two RNNs make up this Artificial Neural Network model. An encoder is present to process the input, while a decoder is responsible for processing the output.
8. What Is An Artificial Neural Network Algorithm?
An Artificial Neural Network program rests on four different types of algorithms:
- Gradient Descent
One of the most popular optimization algorithms used in Machine Learning, Gradient Descent, mostly deals with training a Machine Learning model. It is an efficient algorithm to calculate coefficient values that can reduce the cost function to the greatest extent.
- Newton’s Method
This method uses the Hessian Matrix and is often referred to as a second-order optimization algorithm.
- Conjugate Gradient:
This method is a combination of the Gradient Descent and Newton’s Method. However, it is different from the Gradient Descent in a way that it expedites the slow convergence.
- Quasi-Newton Method
This method counters the computational expense problem associated with the Newton Method. In essence, it is like the Newton Method.
9. Artificial Neural Network Examples
You will find many examples of ANNs on the web. Some of the most common examples of the optimization algorithm include:
- SGD
- ADAM
- HESSIAN FREE
- LBFGS
- RMSPROP
- ADADELTA
You may also consider these examples of activation function to determine the node output:
- CUBE
- ELU
- RELU
- SIGMOID
- TANH
- IDENTITY
10. What Are The Advantages And Disadvantages Of Artificial Neural Network?
Benefits of ANN:
- There is no requirement for formal training in Statistics
- Detection of non-linear relationships between dependent and independent variables is easy
- An array of optimization algorithms are available
- There is easy detection of possible interactions amongst predictor variables
- ANN can work with incomplete knowledge
Drawbacks of ANN:
- ANN is dependent on equipment, especially processors with parallel processing power
- No clear explanation is given by ANN while probing a solution, reducing the network credibility
- ANN depends a lot on trial and error to achieve an appropriate network structure
- ANN can only work with numerical information
- No known duration of the network makes it difficult to work with
Conclusion
Artificial Neural Network tools are rapidly growing and becoming indispensable to all sorts of tasks and functions. ANN algorithms have become the basis of many industrial operations, while the techniques have made efficient information exchange possible. With the advance of technology, the disadvantages of ANN are gradually disappearing. Jigsaw Academy offers a Postgraduate Certificate Program In Artificial Intelligence & Deep Learning. The course is 6 months long and is held via online live sessions on weekends. It includes a lot of case studies for problem-solving and a lot more attractive features that enable learners to gain a comprehensive understanding of Artificial Intelligence, Machine Learning, and Deep Learning.
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5 Best Applications of Artificial Intelligence In Real World
