Machine Learning vs Deep Learning: which one performs better?

Machine learning vs deep learning : In the field of Artificial Intelligence (AI), machine learning and deep learning are often-used algorithms that have shown huge progress over the past few years. Yet, understanding what differentiates them can be slightly tricky.

Hence, let us begin by defining these approaches.

28 juin 2021

What are machine learning and deep learning algorithms ?

To put it simply, machine learning is a subset of AI, the same way deep learning is a subset of machine learning. These two approaches are fundamental for the development of AI as the huge progress they have enabled attests.

What is Machine Learning ?

Machine learning’s main purpose is to train and teach computers how to learn from data. That will enable them to eventually recognise certain patterns when new data and information come in, so that they can make more accurate predictions, thus minimising the probability of making mistakes.

Within machine learning can be found two different approaches, respectively supervised and unsupervised learning. To put it simply, what differentiates them is that, while supervised learning is based on labeled outputs, unsupervised learning is not. More so, supervised learning has an advantage in that it already knows that there is a correlation between input and output data. Overall, machine learning is essentially a concrete application of AI.

And what about Deep Learning ?

Deep learning intends to reproduce what usually occurs within the human brain. In order to do so, it uses artificial neural networks (ANN). ANN refers to “the piece of a computing system designed to simulate the way the human brain analyzes and processes information” (Frankenfield, Investopedia, 2020). These ANNs try to find correlations and to recognise patterns so as to classify and label the information the same way the human brain would. Hence, deep learning is much more independent than machine learning as it does not require that much human intervention. 

Usually, deep learning algorithms perform much better when given a large amount of data. Once they have this, they can then instinctively come up with specific characteristics and classify information. 

What is the difference ?

To completely oppose these two approaches would be irrelevant. Indeed, as stated previously, deep learning is a subset of machine learning. Essentially, deep learning is machine learning. Yet, the two work distinctly. 

First, there is the question of the optimal data requirements to perform well. Deep learning needs billions of data points to provide impressive results and more accurate interpretations. That can be an advantage in the sense that, as opposed to machine learning, it does not reach its limit at a few thousands data points and can go much further. 

Yet, this huge data volume can also be seen as an inconvenience as it implies that, in order to obtain satisfactory results with deep learning, companies must invest in expensive, high-end machines (much more powerful than a mere computer). Thus, one of the cons of deep learning is that it can be extremely pricy. Finally, while machine learning still requires directions, deep learning is self-regulatory and thanks to its ANNs can figure out itself what are the errors it should avoid making.

Which one is the most efficient? 

Now it is time to figure out the outcome of the machine learning vs deep learning battle. In terms of performance, you could argue that deep learning delivers better results than machine learning. 

Yet, as mentioned previously, this comes at a certain price so your company needs to have the necessary budget to invest in that approach. Not only that, but as the graph shows, when it comes to smaller amounts of data, machine learning is much more efficient. It is undeniable that deep learning reflects a huge step forward in the field of AI. Yet, one should not downplay the importance of machine learning. Even if deep learning were to become the norm in a few years time, machine learning would still be frequently used by data scientists, only on smaller data sets. 

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