Machine-learning algorithms adopts statistics to find patterns in colossal amounts of data. Understandingly data besets numbers, words, images, clicks, and many other digital inputs. So long, data is digitally stored; it can be fed into a machine-learning algorithm. Machine Learning is the process that powers many of the services we are used to such as, recommendation systems from Netflix, YouTube, and Spotify. Search engines like Google and Baidu. Social-media feeds like Facebook and Twitter. Voice assistants like Google Home, Siri, and Alexa.
In all of above instances, each platform is collecting as much data about all interactions as possible — what genres one likes to watch, what links are clicked, which statuses individuals are reacting to. Feed these to machine learning systems to make highly educated guess about what customers might want next. In the case of a voice assistant, about which words match best with the funny sounds coming out of individual’s mouth.
Frankly, this process is quite elementary. Find the pattern and apply the algorithm. But it is what today pretty much runs the intelligent digital spectrum, also known as deep learning.
Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to find and amplify even the smallest patterns. This technique is called a deep neural network, deep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.
Neural networks were vaguely inspired by the inner workings of the human brain. The nodes are sort of like neurons, and the network is sort of like the brain itself.
Although idea originated some three decades ago, no one really knew how to train neurons, hence they weren’t producing good results. It took nearly 30 years for the technique to make a comeback.
Supervised, Unsupervised, and Reinforcement
One of the most important to understand is machine and deep learning comes in three flavors: supervised, unsupervised, and reinforcement. In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down targets once it knows the scent it’s after. That’s what folks are doing when one presses play on a Netflix show. We are telling the algorithm to find similar shows.
In unsupervised learning, the data has no labels. The machine just looks for whatever patterns it can find. This is like letting a dog smell tons of different objects and sorting them into groups with similar smells. Unsupervised techniques aren’t as popular because they have less obvious applications. Interestingly, they have gained traction in cyber security.
Lastly, we have reinforcement learning, the latest frontier of machine learning. A reinforcement algorithm learns by trial and error to achieve a clear objective. It tries out lots of different things and is rewarded or penalized depending on whether its behaviors help or hinder it from reaching its objective. This is like giving and withholding treats when teaching a dog a new trick. Reinforcement learning is the basis of Google’s AlphaGo, the program that famously beat the best human players in the complex game of Go.
Machine and deep learning is pretty much what is driving todays’ world. Organizations must think about how to augment growth encompassing different types of learning approaches. It is not all about autonomous cars but many other fields such as sales and marketing, logistics, manufacturing, finance, or internal business processes. Companies are already reaping astounding efficiencies and rewards by investing into deep learning using cloud computing technology and machine learning frameworks. If you are curious about your future business impact and interested in learning about tomorrows possibilities of Machine and Deep Learning solutions, contact IrisLogic today.