Machine learning and Deep learning are the new buzz terms. Although everyone is talking about both and how they are changing the way we work and grow, there is an ambiguity about how machine learning and deep learning are different or whether they mean the same. Hence, Machine learning vs. Deep learning is always a questionable topic. Interestingly, both terms are the result of huge innovations in the field of Artificial Intelligence. The techniques are fundamental in the data science field and are often used interchangeably. Therefore, it is important to understand the differences between the two.
What is Machine Learning
Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from it to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.
It is the state of the art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music service to data security services.
What is Deep Learning
Deep learning is also seen as a subset of machine learning. Deep learning is focused on achieving more power by learning how to represent the world in a hierarchy of concepts. It shows how the concept is related to more straightforward concepts and how there can be less abstract representations for more abstract ones.
It works on the model of continuous data analysis with a logical structure similar to the human brain. It works on many layers of algorithms called Artificial Neural Network (ANN). These networks are identical to the way the biological neural networks of the brain are. Hence deep learning is the field creating ANN that learns and makes intelligent decisions by itself.
In addition to that, like machine learning, it also comes under the category of Artificial Intelligence. But deep learning is related to the part of Artificial Intelligence, which was mostly like humans.
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Machine learning vs. Deep learning -difference between the two concepts
Now that we have seen what Machine learning and Deep Learning are, it is important to really understand how both the techniques differ.
Hardware Requirements:
Machine learning algorithms can efficiently work on traditional low-end machines. On the other hand, Deep learning algorithms require special high-end machines because they need special GPUs. As deep learning algorithms have to perform many operations, special GPUs can fulfill this purpose.
Data Requirements:
Performance as per the scale of data is the main point of difference between deep and machine learning. Deep learning algorithms require huge amounts of data as they don’t perform well on a small data scale. This is because the algorithms understand the detailed data perfectly from large scale data. Machine learning algorithms traditionally can work on their normal rules at any scale of data.
Time consumed:
Deep learning algorithm involves so many parameters that it takes a long time to train completely. It may take weeks for a perfect deep learning algorithm to train completely. On the other hand, machine learning takes very little time to train, varying from seconds to hours. Also, there is a difference in the testing times too. Deep learning algorithms can be tested in very little time. Test time increases as the data size increases. Testing times for machine learning algorithms also vary from small to large testing times.
Approach to Problem Solving:
Deep learning uses the end to end the problem-solving approach. In machine learning, the problem is broken down into several parts, and each part is solved individually. The result is then combined.
Interpretation:
Interpretability of both category algorithms is a big factor of distinction. This is why machine learning is much more popular in the industry as compared to deep learning. Deep learning algorithms’ internal functioning is based on a deep neural network, which is very complex, and hence, it isn’t easy to interpret. The results become complex.
In machine learning, the algorithms simpler and more transparent rules in the form of a decision tree, for instance, and it is simpler to understand. The linear regressions, decision tree, etc., are easy to interpret and are used extensively in the industry.
Conclusion
Machine learning and Deep learning are both significant areas of the Artificial Intelligence field. These techniques are being used everywhere to analyze real-life problems in every industry—Natural Language Processing, medical detections, online media and advertising, computer vision, etc. The big tech giants like Google are using Deep Learning and machine learning for a large number of applications and products being offered. It is important to identify the opportunities where these techniques can be applied, and tasks can become more efficient.
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