The Advantages and Disadvantages of Neural Networks

A promising future tech that is both fascinating and complex, there are many advantages and disadvantages of Neural Networks. Modeled loosely after the human brain, Neural networks are a set of algorithms that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. Neural networks help to cluster and classify. They assist group unlabeled information according to similarities between the instance inputs and classify information when they have to train on a marked dataset.

The technology is applied in various fields such as in financial services, forecasting and marketing research, fraud detection and risk assessment. They also provide the best solutions in problems like image recognition, speech recognition, and natural language processing. Due to the benefits of neural networks in various fields, it has been the hottest buzzword in the field of technology. However, every technology has its promises and its limitations. Here are some pros and cons of Neural Networks.

The Advantages of Neural Networks

Some of the advantages of the neural networks are given below:

  • Data: One of the things that increased the popularity of the neural network is it gathers the massive amount of the data over the last years and decades. Neural networks give a better result when they gather all the data and information whereas traditional machines learning algorithms will reach a level, where more data doesn’t improve the performance.
  • Algorithms: Neural networks are being popular due to the advancement made in the algorithms itself. These recent breakthroughs in the development of algorithms are mostly due to making them run much faster than before, which makes it possible to use more and more data.
  • Ability to work with incomplete information: The data may generate performance after ANN training even with incomplete information. The performance loss here relies on the significance of the missing data.
  • Fault tolerance: Corruption of one or more cells of ANN does not prevent it from generating output. This feature makes the networks fault-tolerant.
  • Dynamic: Neural networks are good to model with nonlinear data with a large number of inputs; for example, images. It is reliable in an approach of tasks involving many features. It works by splitting the problem of classification into a layered network of simpler elements.
  • Parallel processing ability: Artificial neural networks have numerical strength that can perform more than one job at the same time.
  • Computational power: Computer power that is now accessible, enabling us to process more information. According to Ray Kurzweil, a leading figure in Artificial Intelligence, computational power is multiplied by a constant factor for each unit of time (e.g. doubling each year) rather than being incrementally added. This means there is an exponential increase in computational power.

The Disadvantages of Neural Network

The main advantage of the neural network lies in its ability to outperform every machine learning algorithm, but this also goes along with some disadvantages. Here are some of the disadvantages of the neural network.

  • Black box: One of the most distinguishing disadvantages of the neural network is their ‘’black box” nature. It means that we don’t know how and why the neural network came up with a certain output. For instance, when you put an apple’s picture into a neural network and predict it’s a cat, it’s very difficult to comprehend what led this forecast to come up with. When you have human interpretable characteristics, understanding the cause of your error is much simpler. In Comparison, algorithms like Decision trees are very interpretable. This is important because, in some domains, interpretability is quite important.The reason behind some of the banks don’t use the neural network to predict whether it is creditworthy is they need to explain to their customers why they didn’t get a loan. Otherwise, the person may feel wrongly threatened by the Bank, because he cannot understand why he doesn’t get a loan, which could lead him to change his bank. This also implies some of the sites. They can’t decide to delete or keep the account based on the machine learning algorithm.
  • Amount of data: Neural networks require much more data than any other traditional machine learning algorithms, as in at least thousands if not millions of labeled samples. This is a serious problem and many machine learning problems can be solved using fewer data in any other algorithms. This leads to the problem of over-fitting and generalization. The mode relies more on the training data and may be tuned to the data. Although there are some cases where the neural network has a deal with little data, most of the time they don’t.
  • Computationally expensive: Neural networks are computationally expensive than any other traditional algorithms. Most of the traditional machine learning algorithms take much less time to train, ranging from a few minutes to a few hours or days. The amount of computational power that a Neural Network needs depends heavily on the size of your data, but also the depth and complexity of your network.
  • Determination of proper network structure: There is no specific rule for determining the structure of a neural network. The appropriate network structure is achieved through experience and trial and error.
  • The duration of the network is unknown: Reducing the network to a certain value of the sampling error implies completing the training. This value does not offer us the best outcomes.

In Conclusion

Neural networks have been developing very rapidly in this 21st century. In our present day, we have seen several advantages and disadvantages of Neural Networks which has had both achievements as well as problems encountered in the course of their use. The disadvantages of the neural networks can be eliminated one by one with their advantages increasing day by day.

We live in a Machine Learning renaissance because it gets more and more democratized which enables more and more people to build useful products with it. Out there are a lot of problems that can be solved with Machine Learning and it’s sure this will happen in the next few years. One of the main problems is also that only a few people are aware of the neural networks and only a few can understand what can be done with it. In conclusion, neural networks are becoming an increasingly indispensable part of our lives.

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