Deep Learning vs Machine Learning: What’s the Difference

Alyse Falk
Published 06/15/2021
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To begin with, let’s dig into the basics of Machine Learning and Deep Learning. ML is a subset of artificial intelligence that serves to provide the machines with the ability to automatically learn and act based on previous experience. Machine learning involves the “implementation” of different algorithms including neural networks that help to solve the problems.

DL, in its turn, is a subset of machine learning. Deep learning uses the only algorithm-neural network similar to the human neural system to data mining and analyze various factors. None of your website ideas can be brought to life without DL systems.

The definitions of deep learning and machine learning may sound interchangeable for beginners. Both ML and DL fall under the category of artificial intelligence, however, it’s necessary to understand the difference between them.

Read more related articles: Data Analysis, Deep Learning, Genetics, and Pharmaceuticals Drive Innovation in Bioinformatics and Biomedicine  |  Beyond The Basics: How Deep Learning Will Change Automation  |  Deep Learning Meets the Internet of Things: How New Frameworks Will Drive the Next Generation of Mobile Apps


machine learning v deep learning



The Mechanism of Machine Learning

We all like it when our phones sort out the photos in the gallery according to the content. Let’s imagine that we need to “train” the system to differentiate and then sort out the images in the phone gallery between a few categories, cats, dogs, landscapes, and food. To make the ML algorithm do that you first need to make it “recognize” the characteristics of each. But still, the problem remains unsolved: how does the algorithm know to tell the difference between a cat and a rainfall?

The answer to this question is very simple. All you need is to present the system with structured data. You tag the pictures of dogs, landscapes, cats, and food in order to set their characteristics. This data will be enough to train the machine learning algorithm. Consequently, it will continue to work based on what it has learned previously.


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The Concept of Deep Learning

Now, when it’s all clear with machine learning, let’s see how deep learning will solve this issue. DL neural networks will take a different approach to learn to classify the objects.

The main difference of DL is that it doesn’t necessarily need structured/tagged image data to classify the objects into several categories.

In this case, the image data is processed through different layers of neural networks. Then each network hierarchically defines specific features of the images (like furs and tails for animals, flowing water, and grass for landscapes and etc).

This is similar to how our human brain works to classify the objects. The system processes the data through various layers of neural networks, then it finds the appropriate identifiers to classify the required objects.

The above example is a way to make it easier to explain the difference between deep learning and machine learning without using the terms. Technical definitions only confuse people who are not professionals in the field. However, we hope that we have managed to convey the essence.


Analysis of the given example

The key point of machine learning algorithms is structured data. They are used to determining the characteristics inherent in objects. If the nose is small, then the cat. If it’s big – a dog. It is the labeled data that allows ML to study objects, classify and give ready-made solutions.

The deep learning network algorithm is radically different. They don’t use structured data here. Instead, the classification is carried out based on different levels of the network and the information that is accumulated in them. In fact, in deep learning, each layer contains its output. Therefore, when processing images by layers of the network, information is combined, forming a unified classification method.

Considering all the above, there are four key differences between the algorithms. This is fundamental knowledge:

  1. The way data is presented to a particular system. In almost all cases, ML requires well-structured data. However, they are not required for deep learning at all. Processing takes place through the use of layers of artificial neural networks.
  2. The key task for which ML algorithms have been created is learning by understanding the labeled data. It is this function that allows you to use them in the future. As a result of using these, it is possible to obtain many additional results that provide datasets in strikingly large numbers. However, machine learning algorithms are not standalone. Sometimes they require human intervention. Such a measure is necessary if the result obtained is unsatisfactory.
  3. The key task for which DL was created is the reproduction of human intelligence. This is why algorithms do not use tainted data. Also, this particular feature eliminates the need for human intervention in their activities. Deep learning algorithms have nested layers of neural networks that provide data transfer. This is done through the hierarchy of all kinds of concepts. As a result, the layers of neural networks learn from the mistakes made. The flip side of this is the dependence of results on data quality. If its level is insufficient, the conclusions will be incorrect. This is where data quality is everything.


Using deep or machine learning in commerce

Technology is extremely interesting. Not only e-commerce owners are interested in them but also ordinary people who are passionate about science. However, DL and ML are most used for business.

Technologies differ from each other. The differences characteristic of the algorithms determine the greater efficiency of the application of each of them for a certain type of problem. However, if you are only interested in the application of technology in e-commerce, then the recommended directions are not obvious, which is okay. Understanding the fundamental difference between technologies requires a good level of familiarity with them. Therefore, we will highlight the recommendations for use separately.

Businesses should use machine learning when:

  1. In the presence of data that lend themselves to structuring and can be effectively used by ML algorithms.
  2. When AI capabilities are used to identify brand competitors.

It is worth noting that the use of machine learning allows you to implement a huge number of solutions. They are used by business projects in different directions. In particular, it is machine learning that enables identity verification, advertising creation/improvement, marketing improvement, and information gathering. These are colossal opportunities for the future, available today.

Situations when a business should choose deep learning:

  1. The company owns large amounts of data that can be processed, analyzed, and interpreted.
  2. It is required to solve problems that are too complex for machine learning technology.
  3. The company creates software that provides training for DL networks.



So what do we have in the end? ML and DL are the two subsets of its majesty of artificial intelligence. They both present the undeniable value in modern human life but do not work equally to solve the same issues. The key difference between machine and deep learning consists in the way the data is transported to the system. While ML operated based on how it was trained by humans, the DL relies on artificial neural connections and doesn’t need human involvement.


About the Author

Alyse Falk is a freelance writer. She handles stories about the latest developments in the field of technology. Passionate about AI, Alyse has extensive experience writing articles on the system to data mining, analytics, cloud computing, cybersecurity, machine learning, and IoT devices.