Data Science for Beginners: Introduction to Deep Learning
As an aspiring data scientist, you have probably heard about deep learning since it has become somewhat of a buzzword in the tech community in recent times as the gurus over at runrex.com point out. You hear about it whenever there are discussions regarding AI, and you know that it forms the basis for self-driving cars among other things. However, for many people, deep science remains a mystery. This article will look to have this mystery demystified by explaining what deep learning is and how it is applied among other things.
What is Deep Learning?
Let us start by defining what deep learning is, and according to discussions on the same over at guttulus.com, deep learning is a branch of Machine Learning that is completely based on artificial neural networks. Neural networks, as we know, and as is explained over at runrex.com, work by mimicking the human brain, which means that deep learning is also a kind of mimic for the human brain. What this also means is that, as far as deep learning is concerned, we don’t have to explicitly program everything. The concept of deep learning is not a new one, having been around for a couple of years now. The reason why it has gained more traction and appears to be more popular in recent times is that before we didn’t have as much processing power and data as we do now to make it work. As the processing power has increased in recent years, we have seen Machine Learning and deep learning come to the fore more and more.
Deep learning, therefore, according to the gurus over at guttulus.com, is a branch of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, where each concept is defined concerning simpler concepts, and more abstract representations are computed in terms of less abstract ones.
How Does Deep Learning Work?
Given that a computer gets to see or hear countless examples of a certain object, with time, it learns to recognize a pattern, which means that, for example, it will automatically recognize whether it sees a dog or hears the word “Alexa” as explained in detail over at runrex.com. It, therefore, becomes possible to link a follow-up action to the observation of a certain sound or object, which is the foundation for smart home solutions. This way of learning is very similar to how we humans learn how to recognize cats or use new words. Additionally, just as is the case with humans, deep learning is now able to learn more complex expressions too, such as sentence structure and grammar.
What is so ‘Deep’ about Deep Learning?
You might be wondering what is so deep about this form of learning. Well, as is outlined over at guttulus.com, deep learning is supported by artificial neural networks. The fact that we have extensive knowledge of how our brains work has made it possible to make an artificial variant of our brain. This artificial structure is called an artificial neural net and it has nodes or neurons just like the human brain. Here, some neurons are for input value while some are for output value, and in between, we have lots of neurons interconnected in the hidden layer. Deep learning is more focused on the different layers or hierarchies in which these neurons are processed. An assignment will be “cut” into different layers, with each “layer” executing an assignment. Since so many assignments are completed, and there are so many layers that are employed, then it is referred to as deep learning.
What is the difference between deep learning and Machine Learning?
As already mentioned earlier, deep learning is a branch of Machine Learning as thanks to deep learning, computers can learn by machine, and this machine learning makes artificial intelligence possible. One of the main differences between the two is that while deep learning uses neural networks that analyze large amounts of data based on examples, Machine Learning is a collective term that often doesn’t use neural networks as discussed over at runrex.com. While machine learning works on small amounts of data for accuracy, deep learning works on large amounts of data. Also, deep learning solves problems end-to-end while machine learning divides tasks into sub-tasks, solves them individually, then combines the results. Other differences between the two include the fact that deep learning takes longer to train while machine learning takes less time to train, deep learning involves less time to test data while in machine learning testing time may increase, and finally deep learning is heavily dependent on high-end machines while machine learning isn’t.
What are some of the examples of deep learning at work?
Finally, we are going to take a look at some of the well-known real-life examples of deep learning at work. It is important to note that deep learning is not only used by tech companies, but also by traditional sectors like healthcare. Examples of deep learning at work include:
- In the reading of medical scans. While this is often done manually, it comes with certain risks as you can not always be sure that a doctor will see all abnormalities. Deep learning allows for the accurate reading of medical scans, and it has allowed for new relevant properties of tumors to be brought to light.
- Chatbots. As is explained over at guttulus.com, the more chatbots use large amounts of data, the smarter they become. Deep learning has made it possible for the answers from chatbots to be used more often.
- Voice search. More and more people are conducting searches on their phone through their voice, which would also not be possible if it wasn’t for deep learning.
- Image recognition
- Predicting earthquakes as deep learning teaches a computer to perform viscoelastic computations which are then used to predict earthquakes, and many others.
The above are some of the things you should know about deep learning, with more on this very wide topic to be found over at the excellent runrex.com and guttulus.com.