Supercomputers are some of the most valuable and advanced machines on the planet. They can analyze and process billions upon billions of equations in a short period of time. These machines have a wide variety of uses in nearly every sector of academia and the economy. However, they have curiously been underutilized in the field of big data and machine learning. This field thrives off of massive amounts of information and information being brought in from a wide variety of fields. Many of its most important algorithms thrive off of processing power and the ability to create results as quickly as possible. Supercomputers can be utilized to power significant algorithms such as Apriori and Naive Bayes in a way that can make the most of their potential.
How Supercomputers Help Machine Learning
There are two primary types of machine learning. One of these is supervised learning. Supervised learning is a form of learning that is based on an example set. An algorithm runs a number of different processes in order to achieve the original set again. The computer may need to run the algorithm thousands or millions of times. Unsupervised learning is another common form of learning that allows for a greater degree of flexibility. An algorithm continues to process outside of an example set and can be manipulated by a user in order to produce a different set of results.
Artificial Neural Network Framework
These types of learning often rest on a framework such as an artificial neural network. The network changes its weights depending on the factors being considered and the eventual goal of the algorithm. Supercomputers are helpful because of the nature of this process and the ways in which the process depends on time and processing power.
In some instances, an algorithm may take minutes or hours to run once. The algorithm may need to run hundreds and hundreds of times in an ideal state in order to properly reflect the unsupervised desires of the operators or the example set of the supervised learning process. Since developers or programmers may not have much time at all to process the information being considered, they may have to make their algorithm less efficient or work with data with a higher error rate. The supercomputer solves that problem. It allows algorithms to be crafted and processed as effortlessly and efficiently as possible. This process leaves more time to perfect the algorithm and to tweak the system so that it produces the best possible results for the user.
Linear Regression Machine Learning Algorithm
Linear regression is the process by which machine learning and big data help to make sense of a disparate plot of data. The artificial intelligence algorithm begins by inputting a series of numbers that can be represented on a graph as data points. These points are placed onto a plot with an x-axis and a y-axis. In general, they may not have any clear connection. The machine algorithm looks at their relationships and then draws a straight line that shows that relationship.
This straight line can be used to predict a trend or a future set of data that may emerge. The linear regression model is based on the amount of data produced over a period of time. Supercomputers can help a linear regression model consider multiple factors at the same time. They can analyze more data points from more factors than a standard computer. A supercomputer could also draw thousands of linear regression lines and use inputted factors to pick the ones that would be most effective for the algorithm or the question being considered.
Apriori Machine Learning Algorithm
Apriori is an algorithm that helps an individual identify trends and patterns in large sets of data. It works by beginning with a set of rules that extend to a set and help place certain data sets in categories. There may be data sets that have a particular word or fall within a range. Then, the algorithm narrows that set down further. Another rule may be introduced that breaks the new information into smaller and smaller groups and then plots out how those groups are related.
Apriori can devise and determine relationships in data and then use those relationships to plot the eventual development of data over time. Supercomputers are helpful for introducing more and more data sets that utilize more information. A supercomputer could double or triple the basic number of categories that can be used and the numbers in the data set. The error rate could be reduced and the predictive model created by the algorithm could be expanded further out over time.
Naive Bayes Machine Learning Algorithm
The Naive Bayes family of algorithms is a type of algorithm that tracks different characteristics and assigns those characteristics to different groups. This approach is classified as “naive” because it considers only those features which are independent of each other. Dependent variables would require a different set of algorithms that have more complex connections between each other. The algorithm could be used to create two basic categories and analyze an incoming data set over a large number of factors that may be either qualitative or quantitative.
This algorithm can then sort the data set into one of the categories. Supercomputers can help to potentially construct a new category or dozens of new categories. They can introduce a number of wide-ranging variables that can be implemented in each decision about categories. All of this processing can occur much more quickly than with traditional computers.
Supercomputers can be expensive and somewhat cumbersome. But they can be a lifesaver for testing and tweaking algorithms. Using a supercomputer can help companies perfect the usage of algorithms such as linear regression, Apriori, and naive Bayes that those companies rely on. Companies need to be aware of the possibilities of supercomputers and the need to form relationships with those companies that own and utilize these tools. The experienced algorithm company can gain on their competition and greatly advance the cause and performance of machine learning and big data by using these sophisticated tools.