Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence.
On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it. As a result, the more data is being fed into the right algorithm, the more general are the rules and features that it’s able to infer in relation to a given scenario and, therefore, the apter it gets at handling new, similar situations.
Google Translate’s science-fiction-like „Word Lens” function is powered by a deep learning algorithm and Deep Mind’s recent Go victory can also be attributed to DL – although the triumphant algorithm AlphaGo isn’t a pure neural net, but a hybrid, melding deep reinforcement learning with one of the foundational techniques of classical AI — tree-search.
Deep learning is an ample approach to tackling computational problems that are too complicated to solve for simple algorithms, such as image classification or natural language processing. However, its current business uses are very limited. It is quite possible that a large portion of the industries that currently leverage machine learning hold further unexploited potential for deep learning and DL-based approaches can trump current best practices in many of them. For instance, one could read several articles in the past couple of months about how DL is going to revolutionize search, with Google’s former head of AI John Giannandrea taking over the company’s search department (and how this could potentially transform the field of SEO, as a whole).
DEEP LEARNING FUELED RECOMMENDER SYSTEMS – THE FUTURE OF PERSONALIZATION
We are pretty sure that deep learning is going to be the next big leapfrog ahead in the field of personalization as well. Personalization constitutes more and more an area of focus for businesses ranging from eCommerce stores to publishers and marketing agencies due to its proven potential to drive sales, increase engagement and improve overall user experience. If data is the fuel of personalization, than recommender systems are its engine. The advances in these algorithms have a profound effect on the online experiences of users across domains and platforms.
Read the source article at Dataconomy.com.