In the past years, AI became something regular and together with it, deep learning transformed the way people solve problems. Because everybody prefers deep learning nowadays, it is obvious that it became more popular than the classic machine learning.
With all that, machine learning has some advantages when used in the right situations. In this article, we will discuss what are the advantages and disadvantages for both of them and when is the best case to use them.
When deep learning is better than classical machine learning
Find out what are the circumstances when deep learning exceeds machine learning.
Deep learning has the best efficiency in terms of vision, speech or language and there are spheres where machine learning can’t even compete. In the end, deep learning is a better machine learning approach that learns and evolves into something better as you give it more data.
Very effective with data
Compared to machine learning, deep learning machines escalates efficiently while having more data. With deep learning networks, often times all you have to do is use more data to get better results. Machine learning requires more elaborated improvements and usually doesn’t scale so efficient.
No feature engineering required
Usually, machine learning demand feature engineering. For a deep prospect, the analysis is made on a dataset. Sometimes you can choose to reduce the dimension for an easy process and then pick the appropriate features for the algorithm. Deep learning eliminates the feature engineer step and gives you almost instant results.
There are various deep learning approaches and each one of them can be adapted to distinct domains with less effort than with machine learning algorithms.
Machine learning works different for distinct domains and applications and it calls for special research for each field.
When classical machine learning is better than deep learning
There are some situations when machine learning is still better than deep learning. Find out what are the three best regions where classical machine learning is still the king.
Better for small data
As deep learning works better with big data, machine learning is good for when you use small data. For small apps, deep learning is not so relevant and it is also too expensive to use. For when you have a small database, machine learning outruns deep learning.
Few resources needed
As we said above, machine learning is cheaper. Deep learning needs time to learn the data, expensive GPUs, fast CPU, SSDs and so on, all those requiring a lot of money. Machine learning works OK with only a decent CPU and it doesn’t request performant hardware.
Being an old method of analyzing data, machine learning offers easy to interpret insights. Machine learning has everything: a theoretical foundation, deep understanding of data. Deep learning, on the other hand, still gives researchers a headache when they want to understand the full insights.
So here they are. When you have all the resources: money, time and data, better use a deep network. If you just started, a classical machine learning will do just fine. If you want to discuss more on the topic, you can contact us anytime!
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