As much as most people do not understand the difference between deep learning and machine learning, we often hear everyone talk about them. According to Google trend results on the keywords, there is an ever-increasing search for the two words. The big data specialists from ActiveWizards emphasise the need to understand the difference in a clear and simple way before putting the two in to action efficiently. Nothing can illustrate better than explaining the difference between deep learning and machine learning as this article will do.
Basic Understanding of Deep Learning vs. Machine Learning
- Machine learning – in layman’s language, computers use various algorithms, which are set to perform various tasks on a large scale of data and derive sensible interpretation out of it. This is an advancement from specific software set to perform only one task or set data. Therefore, machine learning helps to deal with a large amount of data made up of different variables at the same time. As part of artificial intelligence (AI), some of the common decision making includes logic programming, Bayesian programming and decision tree learning among many others.
- Deep learning – we can all agree that this is a more detailed way of looking at AI and machine learning. It is what makes it possible for IT experts to apply various technologies. For instance, a robot works well because of deep learning knowledge and applications. Every technological advancement we see including fictional and virtual reality is possible because of applications of deep learning.
Handling of Data
Unknown to many, both machine learning and deep learning start to show a difference on how they handle data as the data increases in amount. Machine learning performs better when the amount of data is relatively small since the algorithms are set this way. On the other hand, deep learning shows a better performance when the amount of data is large. The algorithms are set in a way that one data set depends on the other to make sensible meaning of it.
Machine learning is comfortable with the use of simple and traditional hardware. Its compatibility with the hardware is as a result of its simplicity in handling a relatively small amount of data. However, it is also a part of artificial intelligence and can still show good results with new high-end hardware. On the other hand, deep learning with its detailed algorithms and ability to handle large datasets shows better results with the high-end and sophisticated hardware options.
Another difference that is of interest is how the two technologies solve problems. Machine learning works better when the tasks are broken down and tackled separately. The results are then assembled together to give sensible results. On the other hand, deep learning with its powerful algorithms and capabilities prefers to handle its tasks together. It uses a back to back approach that is not only fast but increases accuracy as well.
With a better understanding of the difference between machine learning and deep learning, the companies and organizations that deal with big data can better know what to apply for the best results.