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advantages and disadvantages of deep learning

Advantages & Disadvantages Of Deep Learning

There have been lot of discussions regarding the ability of machines learning to do most of the things humans do at various industry levels. Technology is fast evolving and there is a need to understand their downfalls while being excited about the problems they are capable of solving. The terms artificial intelligence, machine learning and deep learning often leave one perplexed. This article aims to create an understanding of deep learning along with its advantages and disadvantages. Before understanding deep learning, its necessary to have an idea about machine learning. What is machine learning? Machine learning is a part of AI that enables computers to learn from data without being programmed explicitly. While machine learning is has been in use for a long time now, there are some problems that machine learning fails to solve due to which deep learning came into existence.

What is Deep Learning and how does it work?

Deep Learning was developed as a sub field of Machine Learning to solve the compound input-output mappings. The technology is inspired by the human brain and works through neural networks. These neural networks comprise of layers or neurons that are connected adjacently. The number of layers defines the “depth” of the network. These neurons work together to come up with an answer.

So what technologies use Deep Learning? And How is it better than other techniques?

The applications of deep learning are numerous. Organizations across industries are using deep learning neural networks to extract meaning from large amounts of data to increase their revenue by providing innovative products and improvised service to their customers. According to a report by Grand View Research, deep learning market size is estimated to reach $10.2 billion by 2025 and the reason for such a tremendous growth is the advancement in ML algorithms and deep learning chip-sets. Some of the examples of technologies where deep learning is put into use are language recognition, computer vision, text generation, restoring colors in black and white images and autonomous vehicles.

When compared to machine learning and other techniques, deep learning proves to be a better solution in some aspects. Deep learning eliminates the need for a domain expert to reduce the complexity of data being fed into the system in contrast to machine learning where an expert should work on the data before the algorithms can classify it. DL algorithms outperform other techniques when it comes to large amounts of data and solving problems from end-to-end without breaking them down. It also reduces the need for feature engineering.

Deep learning is where it is today due to its ability to create new features from the training dataset with limited features. Its algorithms are capable of creating new tasks to solve the existing ones reducing the amount of human intervention in analyzing big data. Deep learning has improved processing models as compared to classical machine learning and supports unsupervised learning techniques. It also determines the most important features making results of the analysis much more reliable.

Despite its ability to solve the most complex of problems and capture composite relations, there are certain limitations to Deep Learning algorithms. Firstly, the algorithms cannot make decisions beyond what they have been trained on and also cannot deal with problems that dynamically change. They focus on classification and dimensional reduction and are less powerful for long-term planning due to the lack of creativity and imagination. If the neural networks are given incomplete data they will produce wrong results as the algorithms will not be able to predict the missing data. Furthermore, the algorithms cannot draw conclusions in the cross-boundary areas and the estimated outputs cannot be guaranteed to be precise.

Future Scope of Deep Learning

Overall, the future of deep learning seems to be promising. It has already proved to be useful in solving many organisational issues and responsible for great innovations. It has made its impact in industries such as healthcare, marketing and finance among others making markets smarter. Everywhere AI is expected to make its mark, deep learning will follow. Business enterprises will seek to improve their services using AI which brings the need to improve their technological infrastructure, most importantly cloud, to run deep learning algorithms. However, the overhype that deep learning and AI is going through, in terms of innovations such as driverless cars and drone-taxis that are far away from the near future, may lead to a reduced investment in the area. This suggests that it is better to focus on solving current real life problems using deep learning rather than focusing on the numerous possibilities/ innovations it can pave way to.

Advantages And Disadvantages of Deep Learning:

Advantages Disadvantages
Creates new data sets from existing ones Cannot deal with problems that dynamically change
Creates new tasks to reduce human intervention Cannot make decisions beyond what the machines have been fed
Improved processing models Incomplete data may lead to inaccurate results
Capable of dealing with large data sets Less powerful for long-term planning
Increased accuracy in results Cannot draw conclusions in cross-boundary areas
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