What is Artificial Neural Network With Examples

What is an Artificial Neural Network?

In information technology (IT), an artificial neural network (ANN) is a set composed of software or hardware that is patterned to mimic the function of neurons found in human brains. The ANNs — or simply, neural networks are a type of deep learning technology that also fall within the category of artificial intelligence, also known as AI.

Applications for commercial use of such technologies typically concentrate on solving difficult patterns or signal processing issues. Examples of major commercial applications in the last decade include handwriting recognition to process checks and speech-to-text transcription, oil-exploration data analysis, forecasting of weather, and facial recognition.

The development of artificial neural networks dates back to the beginnings of computing. In 1943 mathematical scientists Warren McCulloch and Walter Pitts created a circuitry system to replicate the human brain’s function. It used simple algorithms.

It wasn’t until 2010 that research started to take off again. The trend of big data, where businesses accumulate massive amounts of data and parallel computing, provided data scientists with the necessary training data and the computing power needed to create a complex artificial neural network. In 2012 the neural network could outperform human performance in recognizing images in one of the ImageNet competitions. Since then, the demand for artificial neural networks has grown exponentially, and the technology continues to advance.

What are ANN Function?

An ANN typically involves many processors working in parallel and organized into different levels. The first tier is the one that receives raw input data — similar to the optic nerves used that are used in human visual processing. Each subsequent tier receives an output of the tier before it and not the raw input, similar to how neurons farther away in the optic nerve can receive information from neurons nearer to it. The final tier is its output from the entire system.

Each processing node is a part of its area of expertise, including the things it has observed and any rules it originally programmed or designed on its own. The tiers are interconnected. It means that every node in tier n is likely to be linked to a variety of nodes in tier n-1 and its inputs, as well as tier n+1, which is the source of input data. There could be a single or several nodes within an output layer; from there, the result it generates is available for reading.

Artificial neural networks can be adaptive, meaning they change their behavior as they gain experience from their initial training, and subsequent runs offer more details regarding the universe. The most basic learning model is built around weighting input streams. This is how each node weighs the importance of the input data it received from its predecessors. The inputs that are essential to finding the right answer are weighed higher.

How Neural Networks learn

The ANN is generally trained at the beginning or fed massive amounts of data. Training involves giving input to the network and telling it what it is expected to output. For instance, to create a network that can recognize actors’ faces, the first training could be a set of images that include masks, actors, non-actors, and animal faces. Every input is accompanied by the appropriate identification for the actors, like their names or “not acting” and “not humans” information. The answers provided allow the model to modify its internal weightings to discover how to perform its task better.

For example, the nodes David, Dianne, and Dakota inform that node Ernie that the image they are currently submitting is one from Brad Pitt, but node Durango claims it’s Betty White, and the training program confirms that it’s Pitt; Ernie will decrease the amount of weight it gives to Durango’s input, and will add more weight to David, Dianne, and Dakota.

In setting these rules and making decisions (that is, the decision made by every node about what information to forward to the next level using inputs from the previous tier neutral networks make use of various concepts. They employ gradient-based training, fuzzy logic, genetic algorithm, and Bayesian methods. They could be provided fundamental rules regarding object relationships being modeled.

For instance, the facial recognition device could be instructed to “Eyebrows are located above the eyelids” and “Moustaches are beneath the nose. Mustaches can be found above and/or next to the mouth.” Preloading rules can help make learning quicker and help make the model stronger earlier. However, it can also create assumptions regarding the issue’s essence that could turn out not to be useful and irrelevant or even counterproductive and untrue; choosing the type of rules to incorporate is crucial.

Furthermore, the assumptions made by people when they train algorithms can cause neural networks to increase the impact of cultural biases. Data sets with biases remain a problem for developing systems to find solutions by discovering patterns in the data. The data used to train the algorithm isn’t neutral – and it’s not that many — the algorithm can propagate bias.

Neural networks and machine learning can increase the bias.

Various Types of Neural networks

Neural networks can be described using the terms of their depth and the number of layers that connect an input to output or what is known as hidden layers. The term neural network is almost a synonym for deep learning. They are also identified by the number of hidden nodes in the model or by how many inputs and outputs each one has. The variations in the traditional neural network design permit various kinds of forwarding and backward transmission of information between different levels.

Particular kinds of artificial neural networks comprise:

• Feed-forward neural networks: The most basic types are called Feed-forward neural networks. They transmit information in a single direction via various input points until it is transferred to the node that outputs. The network might or may not contain hidden node layers that make their operation more readable. It’s designed to process huge quantities of noise. This ANN computational model is utilized in computer vision and facial recognition applications.
• Recurrent neural networks: more complex. They store the output of processor nodes and feed the results to the model. That’s how the model can be believed to be able to predict the results of the layers. Every node of the RNN model functions as a memory cell in calculating and implementing the operations. The neural network begins with a similar front-propagation process as a feed-forward network; however, it can then keep the processed data to use later. If the network’s prediction proves wrong, the system learns from mistakes and continues to make the correct prediction through backpropagation. This kind of ANN is often employed in conversions of text-to-speech.
• Convolutional neural network: One of the more well-known models currently used. The neural network computational model employs a variety of multilayer perceptrons. It also has at least one convolutional layer, completely joined or pooled. Convolutional layers generate feature maps that record an image’s region, which is later broken into rectangles and then sent for nonlinear analysis. CNN model is particularly well-known in the field of image recognition. It is used in various of the most sophisticated applications of AI, such as facial recognition, text digitization, and the natural processing of languages. Other applications include paraphrase detection as well as signal processing and image classification.
• Deconvolutional neural networks: utilize a reversed CNN model process. They are designed to uncover hidden elements or signals that might be considered irrelevant to the CNN system’s work. This model of networks can be utilized in image analysis and synthesis.
• Modular neural networks: Modular neural networks are composed of several neural networks that operate independently. The networks don’t interact or interfere with others’ activities in the computation process. This means that large or complicated computations can be completed more effectively.

Advantages of Artificial Neural Networks

The advantages of Artificial Neural Networks are:

• The ability to process parallel data means that the network can perform more than one task simultaneously.
• The information is saved throughout the network and not just in the database.
• Learning and modeling complex, nonlinear relationships allows you to model the real-world connections between output and input.
• Fault tolerance means that the loss within one or several cells in the ANN does not hinder the output from being generated.
• Gradual corruption implies that the network will degrade slowly over time, as opposed to an issue that would destroy the network in a single moment.
• The capability to create output that is not fully understood, with the performance loss determined by how important the information that is missing.
• There are no limitations on input variables like how they should be dispersed.
• Machine learning is the term used to describe how the ANN can gain knowledge from the events and make decisions based on the data.
• The capability to discover hidden relationships in data without imposing any fixed relationship means that an ANN can better understand extremely volatile data as well as non-constant variation.
• The capacity to generalize and infer unobserved relationships between data that have not been seen. ANNs can predict the output of unobserved data.

Disadvantages of Artificial Neural Network

The drawbacks of ANNs are:

• In the absence of a set of guidelines for determining the correct network structure, the correct artificial neural network design is only discovered by trial and error and trial and error.
• The requirement for processors that can handle parallel processing renders neural networks hardware-dependent.
• The network operates on numerical data, meaning all issues must be converted into numerical values before they are displayed to ANN.
• The inability to explain the reasoning for probing solutions is one of the most significant disadvantages of the ANNs. The inability to explain why these solutions work creates distrust within the network.

Application of Artificial Neural Networks

Image recognition is among the first areas where neural networks could be successfully used; however, the applications of this technology have increased to a variety of other fields, including:

• Chatbots
• Natural language processing, translation, and language generation
• Stock market prediction
• Route planning for delivery drivers and optimization
• Development and discovery of drugs

There are several specific areas in which neural networks can be used in the present. They are used most efficiently for all processes that follow specific rules or patterns and contain large quantities of data. Suppose the information is too huge for a human being to understand within a reasonable time. In that case, the process could be an ideal candidate for automation using an artificial neural network.