How Deep Learning Fosters Big Data Analytics

The rapidly emerging science of Deep Learning is an excellent resource if you seek the most powerful Big Data strategies. Big Data analytics builds around achieving volume, variety, velocity, and veracity in extracting the information from sources of data.

The emerging technologies of artificial intelligence (AI) have a prominent role to play in the efforts. Deep Learning strategies employ neural networks, they filter and process data to find the information hidden within the noise. These algorithms learn by example to recognize patterns and integrate the information into visual analytics displays.

Deep Learning To Enhance Big Data Analytics

According to the IEEE, the world processes more than 1.8 Exabytes of data per day. Also, the amount of data that flows around the globe is growing at an exponential rate. Big Data creates large volumes of information and analyzes it to understand changes or anomalies that indicate impending failures or opportunities.

Sensors take in data indiscriminately; deep-learning algorithms can organize and apply the discrimination to differentiate and deliver it to your analytics dashboard to reveal patterns and valuable business intelligence. Deep learning architectures use layered processing to recognize structures and patterns in ways that conventional algorithms cannot.

Digging Into Unstructured Data

There are countless forms of data that you can store digitally, without fitting them into database tables or spreadsheets. But to understand your dataset well enough to organize it requires some algorithm that can identify significant forms well enough to prepare it for analysis.

The Deep Belief Method (DBM) – One deep learning strategy uses the layers of a neural network to compare data to a similar reference that applies labels based on similarity to a known reference sample. DBMs learn by using multiple layers to build up a complete picture of the data.

Convolutional Neural Networks (CNN) – Another approach uses a hierarchy of layers that alternate between discrimination and averaging across increasing scales to build up a recognizable picture of the data.

Deep Learning strategies such as DBM and CNN can classify data in unstructured and unlabeled files. The technology transcribes audio files and extracts the context, recognizes faces in a video feed, reads scanned texts and faxes for insights.

From Deep Learning To Analytics Dashboard

The most interesting data is often harder to categorize. Referring to the diversity of formats as structured and unstructured is like describing the animal kingdom as comprising elephants and all other non-elephant animals. You may need to find the elephants, but you can find opportunities by classifying the other animals too.

Deep learning is still new, but it is becoming a reality for the Big Data businesses that use it to scour unstructured data files for information. Deep learning and other artificial intelligence strategies will help you to capture the value from every source.

The goal of Big Data is to collect information from diverse sources, filter it, and deliver it in real-time. If you are an early adopter of Deep Learning technologies, you stand to gain the first-mover advantage in your chosen field of data analysis. Additionally, you will position yourself to exploit further opportunities, which are sure to come swiftly as the technology matures.

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