Companies looking to develop a full-fledged artificial intelligence solution have to focus on many different subfields. One of those fields is called deep learning, which is similar to machine learning up to a certain extend. However, deep learning is based on a set of algorithms which attempt to model abstractions found in data. A very complex part of developing AI, but one that is well worth exploring.
A Closer Look At Understanding Deep Learning
The entire concept of machine learning is far more complex than people give it credit for. One aspect of machine learning which is getting a lot of attention lately is called deep learning, in which multiple layers exist between data input and data output. Every new layer passes on a modified version of the data input to the next layer, and so on.
As the name suggests, deep learning is based on learning representations of data. For example, a picture looks like an image to our eyes, yet a machine can interpret it as a vector, intensity values per pixel, or as a collection of abstract shapes. It is evident this industry focuses on looking at information from multiple angles, and create different types of results for us to interpret. Deep learning is capable of interpreting data in far more complex manners than we ever could.
To be more specific, deep learning is an industry encompassing artificial neural networks composed of multiple layers. It is an effective cornerstone for building an advanced artificial intelligence in the future, even though there is still a lot of work to be done before this can be achieved. Moreover, to make deep learning even remotely efficient and successful, there is a dire need for big data.
One of the primary reasons why technology startups are exploring deep learning is due to its potential. It is the closest one can get to scientific discovery by using technology and software. The average scientist will not just collect a lot of data and seek to answer every question related to this information. Instead, they come up with a concept to make sense of the deeper meaning behind that information set. Deep learning is capable of doing the same, with any type of data one can imagine.
To put this into an example, most people can understand, deep learning works a bit like a language. We have 26 letters in the alphabet, which are used to create words. Words are then turned into sentences. This process becomes more complex as our society evolves. Deep learning follows this same path, but with data instead of letters. Eventually, it will take some complex bit of data, make sense of it, and create a representation us humans can understand.
What makes this particular industry so appealing to startups and entrepreneurs is how getting involved in deep learning does not require high-end computer expertise. Any knowledge in a domain or a specific discipline will aid one in building new deep learning models. Even people who are well-versed with Excel, but no other programs or coding languages are more than capable of delving into the world of deep learning if they wish to do so.
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