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:
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.
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.
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.
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:
The working of Artificial Neural Networks is based on some standard characteristics, described below:
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.
The main task performed by Neural Networks is to classify patterns or datasets into patterns that are predefined.
With a given input, Neural Networks can produce the expected output with the utmost accuracy.
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.
Neural Networks can be trained to remember and retain data patterns.
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.
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.
Artificial Neural Network is abundantly used across industries and services. Some of the more common industries that widely use ANN are:
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.
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.
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.
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.
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.
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.
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.
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.
An Artificial Neural Network program rests on four different types of algorithms:
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.
This method uses the Hessian Matrix and is often referred to as a second-order optimization algorithm.
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.
This method counters the computational expense problem associated with the Newton Method. In essence, it is like the Newton Method.
You will find many examples of ANNs on the web. Some of the most common examples of the optimization algorithm include:
You may also consider these examples of activation function to determine the node output:
Benefits of ANN:
Drawbacks of ANN:
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.