Advantages and Disadvantages of Neural Networks

Our mind is a network of neurons developed to process various cognitive and mental functions. In a neural network, our mind processes our day-to-day actions, coordinates our actions with other body parts, and keeps us on track with what it is prepared to do. 

 

When it comes to understanding neural networks, our brain, also our biological neural network, is the nearest thing that we can think of as an example to understand the same. 

 

Even though our brain is a web of networks attached to one another, it is important to perceive it as one big network that processes our neural abilities and functions. 

 

While it is important to understand the importance of neural networks in a nutshell, the 3 layers of a neural network add to the essence of its existence and entire significance. 

 

Beginning from the 1st layer, neural networks  combine the power of our neural abilities to process information and create outputs. Similarly, artificial neural networks also accommodate these 3 layers to process information in an organized manner and get started with performing tasks. Here are the following layers of a neural network discussed in brief. 

 

1. Input Layer - The very first layer of a neural network is the input layer. To process any information, it is vital for the computer to input the information that is to be processed. Let us understand this with the help of an example. 

 

Suppose you are going on a vacation with your family. As soon as you hear of this plan, you have an ‘input’ in your brain (neural network) that ingests this information word by word. From here on, this information is sent to the next layer of the network. 

 

2. Hidden Layer - The hidden layer in a neural network is also known as the processing layer. This layer is the most important of all layers and processes the inputs that our brain receives. 

 

As soon as the input is received by the first layer, it is sent across to the next layer of the network. Herein, the information is interpreted, processed, and broken down into smaller components for the brain to make sense of it. 

 

For instance, after hearing that your family is planning a vacation, your brain will analyze the kind of vacation it is going to be, whether or not you are available for the same, and the number of days you are going to be out there. All such bits of information are processed and then passed on to the third layer of the network. 

 

3. Output Layer - The last layer of a neural network is the output layer. Beginning from the input layer, the network starts processing information that ultimately leads to an output. 

 

This output is the same as a cognitive response that we provide to the other person. As our hidden layer processes information, it creates an output to generate a response from our end. 

 

Similarly, artificial neural networks also create output when asked to perform a certain task or a computation under the use of the output layer in a neural network. This is the last layer of the neural network. 

 

Also Read | Basics of Modular Neural Networks

 

 

Advantages of Neural Networks

 

As we have now understood the basics of neural networks and the way they work, let us now dig into the advantages of neural networks. 

 

1. Effective Visual Analysis

 

The very first advantage of neural networks is that they lead to an effective visual analysis. Since an artificial neural network is similar to that of a human’s neural network, it is capable of performing more complex tasks and activities as compared to other machines. 

 

This involves analyzing visual information and segregating it into different categories. A typical example of this advantage is when any website that you visit asks you to prove whether or not you are a robot. 

 

Robots cannot effectively analyze visual information, while humans can successfully do so. This proves that any person logging into a website is a human as s/he is required to differentiate between different images and put images of a certain kind together. 

 

2. Processing of Unorganized Data

 

Another one of the greatest advantages of neural networks is that it is capable of processing unorganized data. Have you ever wondered how artificial intelligence and machine learning organize bits of data? 

 

The answer lies in the ability of neural networks. By processing, segregating, and categorizing unorganized data, artificial neural networks or ANNs can very well organize data. 

 

In collaboration with big data analytics, unorganized data can be structured into a similar pattern and in turn, organized. With the coming of ANNs, the task of organizing unorganized data has particularly gotten a lot easier. 

 

Unlike the traditional times when teams of skilled humans had to invest their days in categorizing unorganized data, today computers can perform the same function in a span of minutes, if not seconds.  

 

3. Adaptive Structure

 

The third advantage of neural networks is that their structure is adaptive in nature. This means that for whatever purpose an ANN is applied, it alters its course of the structure according to the purpose. 

 

From developing the cognitive abilities of a machine to performing complex applications, the structure of the neural networks is subject to change. This is as opposed to the otherwise fairly rigid structures of numerous machine learning algorithms and applications. 

 

Unlike unchangeable structures, artificial neural networks quickly transform, adapt, and adjust to new environments and display their skills accordingly. This also indicates that the kind of training that goes into the training of these networks is comparatively lesser and more accommodative. 

 

4. User-friendly Interface

 

The last advantage among others is that they portray a user-friendly interface. For any machine or artificial equipment to become a success, its interface and usability of it should be user-friendly. 

 

Likewise, it should not be too complex to work with and simple to use throughout. This goes for describing artificial neural networks in the best possible way. With a user-friendly interface, ANNs can be trained without too many complexities. 

 

Likewise, they are capable of adapting their structures which makes them even more helpful for semi-skilled and skilled professionals to operate. This is one of the biggest advantages of this concept as it can easily adjust with any team of professionals aiming to work with it. 

 

There are a variety of varying networks that function independently in the neural network and perform many sub-tasks. There is no requirement for any sort of  interaction amongst each other during the computation process.

 

Also Read | Applications of Neural Networks

 

Disadvantages of Neural Networks

 

Even though the benefits of neural networks outnumber their disadvantages, it is important to consider them and even dig deep into their whereabouts. Let us now read about some of the well-known disadvantages of neural networks. 

 

1. Hardware Requirement

 

Despite their ability to quickly adapt to the changing requirements of the purpose they are supposed to work for, neural networks can be a bit hefty to arrange and organize. This means that they require heavy machinery and hardware equipment to work for any application. 

 

For beginners or those on a tight budget, this might be one of the obstacles of neural networks. Moreover, it can also mean that one has to invest in supplementary things more than the main component of the process. 

 

Thus, artificial neural networks can be a bit problematic when it comes to their hardware setting, organization, and placement. 

 

2. Incomplete Results

 

The second demerit of neural networks is that they can often create incomplete results or outputs. Since ANNs are trained to adapt to the changing applications of neural networks, they are often left untrained for the whole process. 

 

While this seems to be a fairly easy aspect when it comes to the benefits of ANNs, it can quickly turn into a disadvantage as soon as it is time for the output. Due to incomplete results, ANNs have many a time been the talk of the town. With the help of numerous theorems, only a probable value or an estimate can be calculated for such networks. 

 

3. Data Suitability

 

Another one of the challenges of neural networks is that they are highly dependent on the data made available to them. This infers that the efficiency of any neural network is directly proportional to the amount of data it receives to process. 

 

What’s more, ANNs are also affected if the data made available to them is not suitable enough. Thus, artificial neural network algorithms can go wrong while analyzing data available in small amounts and the one that they cannot interpret easily. 

 

Even when these networks are being trained, they should be fed with humongous data to prepare them for the future. If not, then the results can possibly turn out to be faulty and can distort the actual findings of computation, application, or simply a task. 

 

4. Minimal Control

 

While artificial neural networks programs are pretty much advantageous when it comes to organizing unorganized data, they can be highly damaging too. This refers to the minimal control that the trainers have over the actual performance and overall functioning of the ANNs. 

 

From probable value to the unknown steps of working, artificial neural networks are pretty much concealed in their actual structure. This can mean that not much external influence or control can be exerted on these networks to run them as per the user’s convenience. 

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Advantages and Disadvantages of Neural Networks

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Conclusion

 

To sum up, neural networks are just like our brains that receive input, process information, and create output in correspondence with the information received. While this process seems to be fairly easy and simple, it is a lot more complex in reality. 

 

With the help of many tools and applications, ANNs can be trained to be more accurate, modifiable, and reliant when it comes to their results. 

 

Apart from their advantages and disadvantages, the concept of artificial neural network is one-of-a-kind innovation that has radically altered the face of technology and the way machines work.