Deep Learning: The Next Chapter in AI


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As we talk more and more about big data in our technology spectrum, its important to acknowledge the tools and capabilities that this offers. Predictive analysis, improved functionality, targeted marketing are all by-products of the learning mechanisms creating our smarter, more capable networks. Now that these networks have grown in scale, they can begin to learn and evolve in more impressive ways. This “deep learning” is a part of the story of machine learning, and it’s incredibly powerful with exciting prospects for future business development.

What is Deep Learning?

Deep Learning is a subset of machine learning that is used to reference “deep” neural networks. “Deep” here refers to how many layers are in the network. A neural network is a basically an attempt to design an architectural structure using the patterns of the human brain. Deep learning utilizes artificial intelligence (AI) and Machine Learning algorithms within a many-layered (and thus deep) artificial neural network to identify patterns and draw conclusions that are even more powerful and nuanced.

Forget the Plateau, with Deep Learning, More is Better

Traditional machine learning algorithms are subject to a performance plateau at a certain limit. There is discussion among big data about “how big is too big.” At a certain point, gathering additional data for the algorithm does not improve performance or insight, so there should be careful consideration into the resources allocated to gathering massive amounts of big data. A data scientist can help you identify your plateau points so that you know when it’s time to stop feeding your algorithms. On the other hand, with deep learning, there is no plateau. As these neural networks grow larger their performance can increase exponentially. And technology is now able to provide computers that are powerful enough to train the massive neural networks, priming them for specific use-case scenarios.

Supervised or Unsupervised Learning

Deep learning networks excel at supervised learning. This is when an algorithm is trained by supplying both input and output data until the algorithm learns the correct response. This directed learning prepares the network for a specific task or question. Assume, for instance, you wanted to target consumers who enjoy pizza. You might be interested in finding people who have posted photos of pizza on their social media feeds, but that is a time-consuming task. A supervised algorithm could be fed images of pizza and not pizza until it learns to correctly identify which images qualify for your data. At that point, the algorithm can be introduced to unknown images and identify, by itself, which images contain pizza. These supervised learning scenarios have widespread applications in business marketing, customer development, and more.

Deep learning is just another example of how technology and mathematics are growing by leaps and bounds, giving us access to unprecedented insights. These new developments open the doors for exciting business innovation. With powerful tools and insights, business leaders are more prepared than ever before to navigate the quickly evolving business landscape and come out on top.

Dobler Consulting LLC is a leading provider of database services, premier software development, and information technology support, servicing clients ranging from small businesses to FORTUNE companies across multiple industry verticals. For more information about how Dobler Consulting can help you design your data analysis strategy, visit DoblerConsulting.com or call us at +1 (813) 322-3240 (US) /+1 (416) 646-0651 (Canada).