Deep Learning vs. Machine Learning – What is the Key to Unlocking Artificial Intelligence

Deep Learning vs. Machine Learning
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Introduction

AI, AI, and AI. You may have heard this word many times in the news, on tv shows, and at scientific gatherings. This word, AI, is an acronym of two different words, i.e., Artificial Intelligence. AI is a hot topic nowadays, which is taking the whole world by storm. But there are many things associated with this technology that are not true. Many people use the terms machine learning and deep learning interchangeably with this. In reality, both are different, with different meanings and definitions.

Machine learning (ML) and deep learning (DL) are two different techniques being used in modern-day science to make effective decisions. But many researchers confuse these closely related terms with Artificial Intelligence (AI). The aim of today’s article is to shed some light on the differences that exist between DL and ML. But before describing those differences, let’s define all the three key things of today’s article, i.e., AI, ML, and DL. Their definitions will allow you to grab the concepts easily, which will be discussed further.

What are Artificial Intelligence, Machine Learning, and Deep Learning?

As discussed earlier, these three terms differ in meaning and usage. However, the definitions of all the terms are as follows:

Artificial Intelligence (AI)

The concept of AI has been around since 1950. This concept is all about the simulation of human intelligence by machines. The basic principle of this technology is to capture the human intelligence based on which humans have made this much progress. Human intelligence is an ever-changing subject. It is also the trickiest part of the definition of AI. It is because what you perceive as intelligence changes over time, and new technologies are being created every day.

Machine Learning (ML)

Machine learning is a subset of AI. Most of the time, this learning is referred to as a computer’s learning from large amounts of data. Machine learning is mostly more accurate with large data sets. The big data generally facilitate the large data sets. Now, you must be thinking about what big data is. Big data is basically a large amount of user data that different companies collect from their customers and visitors. The researchers, upon collection of this data, send it to machines where the algorithms analyse the data and provide an output. The recommendations you get on Facebook or Netflix are all results of ML.

Deep Learning (DL)

Deep learning – the future of tech companies. DL is the subset of machine learning, and it works on the principle of a human neural network. A human brain has cells and follows a particular network to communicate with other parts of the body. Just as the human brain processes the information and makes the patterns, in deep learning, neural networks give you results without human supervision. Therefore, deep learning is related to replicating the architecture of the human brain. Due to its complexity, most of students are unable to work on its coursework and they left only one option, hiring a coursework writing service UK.

Differences in machine learning and deep learning

After reading the information above, you know what AI, ML, and DL are. You do not know the differences between machine learning and deep learning. You also do not know which of these have the potential to unlock Artificial Intelligence (AI). Hence, a brief description of the differences is as follows:

1.      Data dependencies

The first major difference lies in the data dependency of both techniques. Machine learning algorithms work better on big data. When there is tons of information, the algorithm recognizes the patterns better and gives promising results. On the other hand, deep learning sometimes can be perfect for small and medium datasets. It does not mean that deep learning becomes inactive upon encountering big datasets. It can also process big datasets.

2.      Hardware dependencies

Hardware is one of the essential things to mimic human intelligence. Without proper hardware, you cannot run an analysis and learn about the hidden patterns. Machine learning depends on low-end machines. It means that it can give you output even if your machine is not that powerful. At the same time, deep learning has some other requirements. It requires a powerful machine with GPU. It is because it needs to run complex neural networks and matrix multiplications.

3.      Execution time

The execution time of the analysis is also important. It is because there exists a significant difference between their times. The execution time of machine learning is about a few minutes to hours. The algorithms only take this much time to respond. In the meantime, the response time of deep learning algorithms is way too high. It can take weeks to get an output from those algorithms. The reason behind this is that neural networks have to give weights to the data. The algorithms give output based on those weights, and it takes time.

4.      Interpretability

After the results are obtained from the algorithm, the next stage is to interpret the results. The interpretation is important to make informed decisions. Interpretation in the case of machine learning outputs is easy sometimes. Sometimes, it can be challenging. For example, algorithms like logistics or decision trees are easy to interpret, whereas SVM and XGBoost are almost impossible to interpret. In contrast, the interpretability of the deep learning algorithms is also almost impossible.

5.      Output type

A significant difference lies in deep learning and machine learning output type. In machine learning, you get an output in the form of numerals. The research then classifies and gives scores to the data based on those numbers. On the other hand, the output of deep learning can be in any form free from elements. It can be in the form of free text and sound.

Conclusion

To summarise, deep learning and machine learning both are different. Although deep learning is a subset of machine learning, it has many differences, as listed above. Both DL and ML have the potential to unlock artificial intelligence. With the use of the right hardware and data, the algorithms can better mimic human intelligence. Anyways, I leave the final decision to you. Read the information above and decide which has the potential to unlock AI.

Author Bio:

Robert Fawl is a professional Content Writer based in London, UK. He is an author and blogger with experience in writing different academic assignments. If you need dissertation writing services to write your dissertation, you can contact him.

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