Deep learning helps organizations implement machine learning. That makes it incredibly important for the software development process and the technical development of a new solution.
Among other things, it forms the basis for self-driving cars and innovative solutions in healthcare. But what exactly is deep learning? And how do you apply it? You will discover that – and more – in this blog.
How does it work?
A computer gets to see or hear countless examples of a certain object. As a result, it learns to recognize a pattern, and over time, a computer automatically recognizes whether it sees a cat or hears the word “Alexa”. This makes it possible to link a follow-up action to the observation of a certain object or sound. This is, therefore, an indispensable technique for smart home solutions.
Note that this way of learning is very similar to the way we humans learn how to recognize cats or use new words. Also, like us, deep learning is now able to learn more complex expressions as well, such as grammar and sentence structure.
What is deep in deep learning?
Deep learning is also referred to as deep structured learning and hierarchical learning. What is really so deep about this form of learning? This form of learning is supported by artificial, neural networks. Thanks to our knowledge of how our brain works, we have been able to make an artificial variant of our brain.
The major difference of this artificial neural network is that deep learning is more focused on the different layers or hierarchies in which these neurons are processed. An assignment is “cut” in different layers and each “layer” executes an assignment. Because so many assignments are completed – and so many layers are employed – we speak of deep learning.
What is the difference between machine learning?
Deep learning is in fact a part of machine learning. Thanks to deep learning, it is possible to let computers learn by machine. This machine learning in turn makes artificial intelligence possible. Deep learning uses neural networks that analyze large amounts of data based on examples. Machine learning, on the other hand, is a collective term that often does not use neural networks.
So you should see deep learning as a Gazelle or Batavus, namely as two examples of a bicycle. Deep learning is a good example of machine learning. A bicycle is again a well-known means of transporting people, just as artificial intelligence is a well-known means of allowing computers to perform human actions.
|Deep Learning||Machine Learning|
|Unfortunately and mistakenly confused with AI / Artificial Intelligence||V||V|
|Makes use of neural networks||V||O|
|Is made to develop algorithms||O||V|
|Learns due to recognizing patterns||V||O|
More information on machine learning you can find in our machine learning blog.
What are well-known examples of deep learning?
Thanks to deep learning, many new applications are possible. Not only tech companies use deep learning, but traditional sectors such as healthcare also see the added value of this technology. These are well-known and appealing examples of deep learning, which may also inspire you to get started with this technique:
– There are various startups that focus on automatically recognizing language expressions. Consider for example Travis de Tolk and Zoi Meet, which translates a spoken sentence (Travis) or transcribes simultaneously (Zoi Meet). The spoken searches on your phone would also not be possible without deep learning;
– Reading medical scans is often done manually. This entails risks: it is not always certain whether a doctor will see every abnormality. Thanks to analyzes based on deep learning, various new relevant properties of tumors also came to light;
– Chatbots become smarter as they use large amounts of data. The more conversations and linguistic interactions that are “conducted” by the bot, the more human the interaction of the bot with its users is. The answers from a chatbot can also be used more often thanks to deep learning.
What are the benefits of deep learning?
Deep learning is sometimes better able to identify a certain phenomenon than a human being can do. That is not as meaningful when it comes to spotting cats in YouTube videos. However, when it comes to signaling irregular cells or other cancer indicators, deep learning can make a real difference in people’s lives.
Note that there are ethical objections to deep learning. Consider, for example, drones that independently make decisions and carry out attacks on specific targets. Computers that make independent decisions ensure that we are less burdened and have more time for other activities. In some cases, however, the question is whether we should have computers make these decisions themselves, for example when it comes to the life or death of people.
The fact that we have to invest less time and effort in certain activities saves organizations FTEs. The more data and computing power becomes available, the more work can be taken over by computers that learn thanks to deep learning. It makes organizations more efficient.
Deep learning is a promising technique that, thanks to pattern recognition, makes it possible to teach computers something. This part of machine learning mimics a human neural network. This artificial neural network saves us time because it prevents people from having to perform repetitive actions. This technique reduces the risk of human error and therefore has absolute added value.