Deep Learning vs Machine Learning

We often get a lot of questions from clients who want to know the difference between Deep Learning and Machine Learning. Although these concepts have many similarities, they are two different things. They both serve different purposes depending on what you want to achieve. 

Understanding the similarities and differences between them can be helpful when implementing data-intensive features in an app. In this post, we solve the Deep Learning vs Machine Learning debate once and for all. That way, you will be able to make the best out of their powerful algorithms. 

How Does AI Learn?

The first thing you need to know to understand the difference between Deep Learning and Machine Learning is how Artificial Intelligence (AI) learns. Technology works differently to human brains, so don’t think that learning occurs in the same way as for humans. A computer cannot easily recognize things that a human can, but they can perform complex operations that humans cannot do. This seems like a contradiction, but it comes down to the principles of computer science. 

In general, computers only do what developers tell them to do. Code is not intuitive, it just follows orders. This also applies to AI technologies. Although most people have the idea that computers are able to do the same things as humans, and sometimes more, there are in practice many limitations. 

There is still a long way to go before machines can implement Artificial General Intelligence (AGI), or in other words, machines are unable to do the same things that humans can; this is in part because computers lack agency. Instead, computers are able to do specific things, some of them being very complex for a human to perform; that’s why, in practical terms, AI resembles more what is known as Artificial Narrow Intelligence (ANI).

This form of Artificial Intelligence is able to learn a specific task or execute it based on a narrow set of goals established by programmers. Through human input, a computer can learn a specific task. Tasks like identifying an image of a cat are easy for a machine that has been programmed to do so. However, this same task may be very difficult, though not impossible, for a machine whose goal is to identify dogs. 

When two different cat detecting algorithms are confronted with another, the one with the best results will tend to be implemented over the other one. That is, one cat detecting algorithm is preferred over the other based on results. Just like natural selection operates in the natural kingdom, so does an analogous form of algorithm selection work with computer programs. 

Thus, when thinking about a specific task, it is more convenient to think in terms of which algorithm can perform this better given the resources available. Some techniques may be better suited than others given the context. 

Is Machine Learning Part of Artificial Intelligence?

With this in mind, we can now move on to the next concept. Machine Learning is a specific form of AI that is able to perform specific tasks under certain conditions. Its main feature is its ability to learn from different forms of structured and unstructured data, something that other AI technologies cannot do; not all forms of AI are programmed to learn.

In general, Machine Learning is a data-intensive way in which computers use available information to achieve a specific goal. By identifying what’s right and what isn’t, it is possible for ML algorithms to do what they are told. That way, a retail app can be programmed to calculate a forecast based on a user’s consumption patterns. Similarly, a recommendation algorithm can be used to learn from users’ preferences, helping developers implement powerful features in diverse industries. In this article you can read about some uses of Machine Learning for web and mobile apps.

Is Deep Learning a Subset of Machine Learning?

By now you have probably noticed that we are going from the general technologies (AI) to the specific ones (ML). Just like Machine Learning is a subset of AI, so is Deep Learning a subset of Machine Learning. This has some implications in terms of how developers understand them.

Although it is common to refer to Machine Learning and Deep Learning as two contrasting technologies, the latter is a subset of the former. That is, Deep Learning is a subset of Machine Learning. Although Deep Learning algorithms are very powerful and have gained popularity in recent years, not all Machine Learning technologies use them. 

The Deep Learning and Machine Learning Difference

That one concept is a subset of the other automatically establishes some similarities. However, there are also important differences between them. Probably the most important difference is that Deep Learning uses layers of algorithms in order to learn from data. These algorithmic layers are known as neural networks. Their main purpose is to make sense out of data without having to depend on human input. 

A neural network is made up of many different building blocks known as perceptrons. Each perceptron is a neuron or unit of the network, and they serve to make different calculations through different layers of calculations. Thus, by making use of data input, it can produce a relevant outcome. 

This technique makes it possible for a computer to learn from unstructured data, using millions of data points, and in an unsupervised way with minimum human input. In short, unstructured data does not have an easily recognizable structure in the eyes of computers. This includes data from photos, videos, audio, and the likes. 

Although, in theory, Machine Learning algorithms can do the same thing as Deep Learning ones, it is common to think of them as being two different and opposing things. That is why developers often refer to Machine Learning algorithms as being able to learn exclusively from a manageable set of structured data and in a supervised manner through labeled data. 

When to Use Deep Learning vs Machine Learning

One of the most obvious factors that indicate when to use one technique or the other is the size of the data set. Because neural networks can be used to analyze huge amounts of data with high levels of complexity, Deep Learning offers a better alternative to this type of data-intensive problems. 

On the contrary, Machine Learning can be used for problems that show a lower level of complexity and where human interaction occurs at some point. This input is used as a way to tell the algorithm useful information that can be stored and used later to make calculations. 

Common app features like forecasts, recommendations, or searches tend to use Machine Learning, although this is not always the case. Lastly, the decision of which one to use depends on the nature of what you are trying to do and the resources available. Having a proper data pipeline is essential to both technologies; make sure to find the Machine Learning service provider.

Artificial Intelligence and Deep Learning

Because of the importance of neural networks in modern computing and their constantly increasing power, it is worth examining how AI will most likely evolve. For many experts in the field, it is impossible to conceive the future of AI without talking about Deep Learning. It seems that AI and Deep Learning will be deeply intertwined for years to come unless other technology takes its place. For the time being, it seems that we should keep an eye on them. 

Machine Learning, AI, and Deep Learning

The future of technology lies heavily on how these technologies evolve. There is still a lot to be done in terms of implementing them in practical use cases. To take advantage of them, companies need to use state of the art computing resources and work with the best development teams available. 

By pairing up with an expert development company, your business can reap the benefits of data in ways that you would never have imagined. At Koombea we’ve helped our clients implement powerful HiTech Machine Learning and Deep Learning features into their apps with great success. Contact us for a free consultation.