What’s Deep Learning: The Basics You Need to Know

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(Newswire.net — October 8, 2019) — We’re living in exciting times, where a lot of technologies and features we used to see in science fiction movies and TV shows are starting to become a reality. In fact, many of today’s developments seem to even go beyond what science fiction writers were able to imagine. Case in point – deep learning. Perhaps one of the most astounding technologies around, this subset of machine learning is already among us and it promises to change everything.

That’s far from being an exaggeration. Development teams, tech enthusiasts, futurists, and even IT outsource services all agree that these neural networks that mimic how our brains work will revolutionize practically all industries. That’s why a lot of developers are working on devices, apps, and systems that leverage that powerful technology to analyze and decide actions on their own.

From driverless cars to Facebook, a lot of things already depend on deep learning. But what is it, really? Let’s review some of the basics you need to know.

Deep Learning, defined

As mentioned above, deep learning is a subset of machine learning which, in turn, is part of a broader category we all know as Artificial Intelligence (AI). Deep learning, in particular, seeks to replicate how the human brain functions when dealing with that. Thus, systems that use this technology are capable of analyzing large data sets to detect patterns, get insights and, ultimately, make decisions based on that information.

The craziest thing about it? Those decisions are made based on the system’s own logic, which evaluates multiple possibilities and defines which course of action is best based on that data. As it happens with a human brain, deep learning is constantly finding new paths for improvement or learning.

Deep learning does so thanks to the superposition of different layers, from the input and hidden layers to the output one. Each of those layers processes and analyzes the data to find new patterns and generate new information. Then, the new information is passed onto the next layer that does the same thing. The process of analyzing and re-analyzing that data is what makes this complex technology and what makes experts think about it like neural networks.

The analysis is refined the more it’s used. The algorithms at the core of deep learning are adjusted according to the input data and the output result. In layman’s terms, the whole system becomes more experienced the more it’s used.

Where does all that data come from?

Interestingly enough, we’re living in the only time where deep learning could be possible. Though it was devised in the 80s, just today we have enough computational power for a digitized neural network to work properly. That’s not all. This is the only time in history where so much data is so readily available. The rise of social media, the ubiquitous use of search engines, the frenzy over e-commerce stores, and the popularization of cloud-based platforms, among others, made it possible for experts and companies to have a lot of unstructured data to work with.

That unstructured data couldn’t be processed by humans since it would take forever for someone to get insight from it – if any at all. That’s why developer teams and software outsourcing companies turned to AI to make the most out of that data and deep learning was born. 

Wait, so deep learning is machine learning?

Somewhat yes, but ultimately no. Though a lot of people hear about them and think they are one and the same, that’s not true. Yes, you could say that deep learning is machine learning, but only if by that you meant that deep learning is one part of machine learning, a subset. Machine learning is a broader concept that encompasses other technologies. 

In addition, there’s a conceptual difference between one and the other. Machine learning is a series of algorithms designed to get progressively better at a specific task or series of tasks but it requires human guidance. In other words, if the algorithm of a machine learning system isn’t working as intended, a developer has to make adjustments to it. In deep learning, the algorithms themselves are the ones responsible for the evaluation of their performance and the adjustments needed.

Some deep learning uses

The best way to understand what’s deep learning is to see it in action. Fortunately, several companies put their in-house teams or outsource development to work in deep learning systems to enhance their products and, ultimately, their entire industries. Taking a look at some of those examples will surely paint you a clearer picture.

  • Siri, Cortana, Alexa and other virtual assistants are based on deep learning. By using neural networks, these systems understand what their users are saying and can carry out the actions that the users are requiring from them. As you may have noticed if you used one of these assistants, they get better and better the more you use them, simply because they make those automatic adjustments to their basic logic to better suit your needs.
  • Though they still have to hit the markets on a massive scale, several successful driverless vehicles are being developed around the world. And that development would be impossible without deep learning. That’s because these vehicles need to understand their surroundings to quickly react in a proper manner. Thus, deep learning allows them to interpret road signs, anticipate movements from vehicles and pedestrians, and even course-correct their movements when something unexpected (like an accident) happens.
  • Deep learning is proving to be very effective to deal with customer service matters. Chatbots, one of the hottest tools around, are one of the reasons why we can claim that. These digital assistants have opened a direct channel between clients and brands to easily talk with each other around certain topics – and without the need for the intervention of a human employee. Chatbots can interact with people’s questions and doubts, answering them in a human-like way, offering quick answers and working 24/7.
  • It may be at the center of a lot of controversies, but facial recognition technology is still taking strides to become a future standard for modern devices and systems. And this tech depends largely on deep learning to work. From Facebook’s automatic tags to its use in security systems for public spaces, face recognition is being tested in a number of ways around the world, even with all the concerns about its possible impact on citizen privacy.
  • We all know that technologies sooner or later end up serving the entertainment industry – and deep learning isn’t an exception. Netflix’s recommendation system uses it to learn about the users’ tastes and habits to better suggest content to watch. Spotify has a similar feature to suggest artists based on listening history and moods. Even Amazon has deep learning at the core of its famous recommendation engine, capable of offering similar and complementary items to the ones already bought by the user.
  • Deep learning is also being introduced in one of the most technological industries out there: the medical field. Through neural networks, several institutions are researching new cures and treatments to fight a lot of diseases. Current efforts are focusing on the early diagnosis of tumors and cancer and the creation of personalized medicines that could open the door for tailor-made medicine that would highly improve the effectiveness of treatments.

Some final words

Deep learning, as everything related to AI, is still finding its way and developing its potential. Many companies are using the various software outsourcing models or working with their own teams to develop solutions capable of taking advantage of one of the most powerful pieces of technologies you’ll see in a long time. 

If you take a closer look at the developments around deep learning, you’ll quickly realize that this technology has come to revolutionize the way we live. Deep learning brings a lot of possibilities to real-life scenarios across many industries, and we are only starting to grasp that. In fact, deep learning itself could suggest new paths and opportunities we can’t imagine right now. Exciting times are laying ahead of us, and we won’t have to wait that long to live them.