“The design of convolutional neural networks follows the discovery of visual mechanisms in living organisms.” Shamane Siriwardhana, Software Engineer.
The human brain is a complex thing. Within seconds, it can identify the meaning of words, symbols, and numbers. It knows that a circle and a curve downward forms a “9”, invert it and it forms a “6’, each with their own meanings that the human brain can now pick up at a glance. This process is so simple that we tend to forget the intricacy of it, only to remember it when we are trying to replicate it with technology.
The ability to process information like a biological brain: that is the concept of neural networks. Think of it as a sketch of how our brains work but instead of lines, it uses mathematical equations. It’s a set of filters, with each stage getting more complicated with each step.
For years, technology is trying to catch up with the supercomputer inside our skulls, but finally we are here: machine learning.
Neural networks are the backbone of machine learning. For example, sorting and tagging systems for websites, management systems, and digital libraries all use neural networks as a foundation.
How emails filter spams, how websites auto tag people to pictures, how a camera recognizes that a cat is cat — these are products of years of developing neural networks. In his article “Why Convolutional Neural Networks,” Shamane Siriwardhana, software engineer, discussed the power of convolutional neural networks for deep learning.
Much like the biological brain, neural networks also have their “neurons” that processes information. With deep learning technology, networks use sets of neurons to “grab” the information and another set to “identify” it. Basically, one huge group to grabs bits and pieces and a few higher ups to look at the whole picture.
We’re still seeing developments every day in the field of machine learning. Enadoc for example is even developing machine learning to help in tagging and management systems. With the backing of Azure for seamless integration with data and processing power, the playing field is now bigger than ever.
You can read more about convolutional neural networks and deep learning here.
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