When choosing the type of AI for the new software product many compare deep learning vs machine learning and ask themselves what is the difference. In this article, we’ll explore those two approaches to AI implementation and give the all-encompassing answer to that question.

With the rise of the Artificial Intelligence or simply AI we can now do things we have never done before. Basically, AI is the set of algorithms responsible for making a decision or a prediction about the next required tasks. The data is analyzed by those algorithms, and as a result, it is represented by the proper and quick decisions.

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You definitely faced the usage of AI, Machine Learning and Deep Learning in your everyday life. Many software companies already use them to provide the best machine assistance to their users. Do you remember those moments when YouTube suggests the next video to watch, or Facebook suggested you mark some of your friends who were on the photo you’ve just uploaded? Here we explain what is the difference between Machine Learning and Deep Learning, their usage, features, capabilities and how it’s related to AI.


What is AI?

Artificial Intelligence is a set of algorithms that include only code and math. Their task is to make the decision concerning the data. The AI methods include learning (the specific rules are used to require information), reasoning (these rules are managed to make relative or certain outcomes) as well as self-correction. AI technologies include Robotic Process Automation (PRA), Machine Learning and Deep Learning, Natural language processing (NLP), and Robotics. AI is broadly applied to different spheres such as healthcare, FinTech, manufacturing, law and so on. Our article is dedicated mainly on the Machine and Deep Learning, so let us focus on these two.

While Machine Learning and Deep Learning are results of AI development – both of them can be considered as an AI but characterized by different features, various tasks, and the number of advantages.

The scheme comparison deep leaning and machine learning as AI types

Some people might think that about that as AI vs Machine Learning vs Deep Learning. Actually, this is not correct from a technical standpoint. As we have mentioned before, both Machine Learning and Deep Learning are the types of AI. So, let us name a few facts that show the difference between Machine Learning and Deep Learning:

First, the algorithms of Machine Learning examine the data and learn to make a decision due to the knowledge they have obtained. However, while doing that, they still need the assistance of human specialists.

Second, the Deep Learning algorithms operate in a similar way, also learn from the given data, although these algorithms are organized in layers and capable of making own rational decision without human assistance. Working with Deep Learning, we deal with the creation of the artificial neural networks, where the work patterns of the human brain are applied to machines.

Third, Deep Learning is the type of Machine Learning, whereas its algorithms have established a lot of the records in own decision making and characterized by different capabilities. Deep Learning is the most powerful type of AI, that even can overcome own achievements in the future.

That’s why these two cannot be separate or opposite.


What is Deep Learning?

As mentioned above Deep Learning is the advanced type of Machine Learning, whereas the functioning is very similar, yet the capability of Deep Learning is quite different. Nevertheless, Machine Learning constantly progresses, this model still needs human assistance. It provides the prediction, later the expert makes the required adaptations. Deep Learning algorithms have the ability to detect the predictions or conclusion are right or wrong on their own. In this case, we can say it is capable of making logical conclusions like human beings by applying the artificial neural network. It is a layered algorithm architecture which works similarly to the human brain, therefore it has the biggest number of capabilities. On the one hand, it is the complicated task to make sure that deep learning model (sometimes called the neural model) always chooses the correct predictions. But once it succeeds, it is a kind of a scientific miracle.

Again, Deep Learning vs Machine Learning: for instance, the Machine Learning-based AI for computer board game can only make moves that it has “seen” before, but combining moves in new or the most efficient manner. Deep Learning AI competes with intuition as well as quick-witted intellect, makes unpredictable or completely new moves, thus becomes the winner and also the best player. Those capabilities are the advantages of modern technologies, it can provide unexpected results of usage in different spheres, like education, medicine, business, etc. It is the future of Machine Learning algorithms.

What is Machine Learning?

First, let’s concentrate our attention on how it generally works. Machine learning (ML) is responsible for making the decisions, but algorithms not only analyze the data, they also learn from this data and use the conclusions for future actions. The more it is doing that, the better it becomes in its tasks. But only in the particular set of tasks. It doesn’t recognize the context and the abstractions. It is still more like a robot, but a very smart robot that can learn to do its job better and faster with the time. However, without going beyond its functions.

Machine Learning has numerous algorithms and differs by the ability to adjust its actions within a certain limit of functions when it is disclosed to another data. As a relatively advanced type of AI, Machine Learning is effective on its own and it does not always demand the interference of the human expert to deal with particular changes. However, it is needed to be “told” whether its predictions were true or false. This gives the possibility not to rely only on the commands of experts for each small adjustment. The computer program learns from its own experience of certain tasks and their performance and later improves based on this experience.

So, when to use Deep Learning vs Machine Learning? For example, if we set some function for the program, giving the required data, it will perform it.  Let’s find out how it works, as for the illustration we can take music streaming services. We always wonder how our favorite songs or artists are happened to be recommended for us, actually, these are machine learning algorithms combine our preferences with the playlist of people who listens to similar music.

The difference between machine learning and deep learning is in that ML is capable of suggesting you the music you’re most likely to enjoy, but within your know preferences like genre, bands, instruments etc. However, Deep Learning is capable of recommending you the music you might like, but from a different genre, which is a huge step forward. But, we’ll talk about that a bit later. 

Machine Learning performs various automated assignments and broadly used in numerous industries, usually proving customer services. Thus Machine Learning has recommended itself as the personal assistant for a lot of users.
Let us see the characteristics of Deep Learning vs Machine Learning.


What is the difference between deep learning and machine learning? We could find it out if we realize their working structures and the advantages they provide for their users. Deep Learning could be mentioned in the context of artificial neural networks, because of the number of own accurate decisions for various tasks, like prediction, recognition, assistance, recommendation, etc. These artificial neural networks consist of multiple layers, which have the special features of recombining learning from one layer to another. Consequently, artificial neural networks have more chances to provide more complex features and train more intensively. And as a result of the deep learning capabilities, it constantly improves without special programming or direct assistance over time. The main requirements are the training time and performance of machine approach tasks.

deep learning vs machine learning - machine learning scheme

In a nutshell, we concentrate on machine learning vs neural networks to find out the difference. Their functioning is quite similar, but they are distinct in final capabilities. Machine Learning can make the conclusion from the learned data but with the certain help of the human. But it is still the performance of the machine using the algorithms and the assistance of human being. Machine Learning predicts the results, the professional task of the human experts will be to clarify and help them perform with greater precision. Anyway, it is the optimization of machine performance and qualitative operations.

Netflix is one of the examples of machine learning applying. Suggesting the next show or movie to watch, the Machine Learning algorithms analyze previous choices of you as well as preferences of users with similar tastes. They make the suggestion of taking into account the reactions of the user and succeed in implementing smart entertainment. Regarding deep learning the process of making conclusions goes much more further. Its algorithms act similarly to the human brain performance with the main task to reflect all activities. The structure of Deep Learning provides the chance to analyze a big amount of the data while being educated by it for the performance of the various tasks. The main achievement of the machines is the decomposition of the data and task assignments. Both Machine learning and Deep learning analyze the data and learn from it, but only deep learning tries to copy the activities of the human brain when it has to make the conclusion. It is all about the real independence of the machines.

deep learning vs machine learning - deep learning schema

As for the Machine Learning vs Deep Learning examples – let’s imagine that we gave them the same task and predict what will be the difference in output. For instance, AlphaGo DeepMind is Google’s Deep Learning AI created to play, learn and finally beat human players in the Go board game, that is considered to be way more difficult for computers than the regular chess for example.

This program really succeeded in playing, and what’s more, gained the great experience from playing with the professionals, made the moves without any assistance and started to play at the unexpected level, and became itself even one of the best players of this game. So, we can say that Deep Learning is not something that human experts are able to guarantee or even predict. It just a matter of time to find out what are its possible capabilities.


The constant development of AI gives new possibilities for machine development. Deep Learning vs Machine Learning, but they are considered to be the subcategories of Artificial intelligence. Both Machine Learning and Deep Learning are the special algorithms that can perform certain tasks, distinguished by their own advantages. The Machine Learning algorithms are capable of analyzing and learning from the provided data, and ready to make a final decision with little but still help of the human assistant while deep learning doesn’t need that much assistance because of basic emulation of human brain workflow and understanding of the context.

Due to constant development, we can talk about deep learning as a type of machine learning, which is identified by self-sufficient decision making which has opened wider use and keep on learning, developing and succeeding in various tasks. These are the high-tech capabilities, new advantages and tech innovations that can happen any time: tomorrow, over the coming weeks or years. But, for sure, it uncovers big possibilities for both machines and human beings. We hope that this article helped you understand what exactly are machine learning and deep learning, find the difference between them.

Now, when you have learned the basics of those technologies, it’s time to think about how they can improve your software product or help your business. If you’re looking for the professional software development company capable of finding the best way those technologies can be leveraged for your business case and implementing it, you can reach us out at our contact page or start the dialogue in the chat widget on the right. Existek is a software house with extensive experience of AI implementation and we’ll be happy to assist you in the development.

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