Even though the term ‘deep learning’ sounds like a new emerging technology, it is in fact a long standing theory that has only just started to find its feet in the modern world.
Deep learning began as an idea, a theory that the neural networks of a brain could be artificially produced and, with time, work much like a human infant mind works. But back in the 1980’s, when such theories were taking shape, the computer technology simply couldn’t keep up. Nonetheless, scientists powered through and continued their work in hopes that one day they could be validated.
Well, that day has come. Thanks to more powerful Graphic Processing Units (GPU’s), and large data sets, deep learning has found its place. Nowadays, technology such as what appears to be a basic part of any smartphone (voice recognition/search, voice commands) is imbedded in your phone through deep learning algorithms based on computational neural networks (CNN’s).
But what exactly is Deep Learning?
Deep learning is a sub category of machine learning that learns from and understands data. It differs from machine learning in the sense that deep learning doesn’t need the data to be manually categorised before it can analyse it. In simple terms, deep learning learns like a child’s mind learns. It is built from deep neural networks that consist of nodes that work together to form meaning from the data it receives. Where machine learning uses one layered neural nets, deep learning algorithms use hundreds.
The magic behind deep learning, is the simplicity. Rather than imputing thousands of lines of code, all that is needed is one line of code that tells the neural network to learn. The network has one goal in mind – to understand the data it receives – and rather than blindly trusting what each neural network comes up with, it self corrects. If it is looking to identify a dog and it comes across a wolf, the machine can autonomously find irregularities with that image and set itself back onto the right track.
Why deep learning?
On the surface, deep learning is surpassing other algorithms because it has the capacity to use vast amounts of data in an effective way. Machine learning algorithms appear to have plateaued even with a surge of data. This means that regardless of data growth, machine learning cannot use it all. Deep learning however, can continue to develop and grow alongside the data as it too increases.
Because deep learning uses many hidden layers, the output has a much higher level of accuracy. This is perfect for moments where accuracy is vital.
How does deep learning affect you?
In this modern age of data, the possibilities of deep learning capabilities are ever growing. As the amount of data accumulated increases, the ability for deep learning to utilise that data increases. The more data the machines have, the more they can learn and the more applications of deep learning can open up.
Anything from voice assistants, to recommended products and everything in between is done though deep learning. Did you ever wonder how amazon always knows that one item you need? Deep learning. Did you ever wonder how you always manage to find the right show or song at the right time? Deep learning.
This system is already affecting everyday life, and the future possibilities are seemingly boundless.