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3 Ways to Accelerate Deep Learning with Data
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Hi! My name is Andrew and I’m an IT professional.

Today, massive amounts of data are being created, driven by billions of sensors all around us. With this data, artificial intelligence, machine learning, big data, and deep learning are all giving us unprecedented insights into just about everything.

The ways we collect data are increasing and the data we are collecting is becoming much more accurate. Thanks to these advances in data collection, we are now able to accelerate deep learning with data.

#1. Big Data

Data is the most important asset in an organization and while deep learning technology and neural networks have been around for a long time, many organizations are just now starting to realize the value of big data. Thanks to advances in technology though, with the right platform, there is no such thing as too much data and no matter the amount you add, the platform will always be able to gather insights from the data you input.

#2. GPU

Modern computers typically consist of multi-core CPUs or GPUs. Today, both multi-core CPUs and GPUs are used to accelerate deep learning, analytics, and engineering applications—enabling data scientists, researchers, and engineers to tackle challenges that were once impossible. As new deep learning algorithms leverage massively parallel neural networks inspired by the human brain, these algorithms write their own software by learning from many examples and deliver super-human accuracy for common tasks like the image, video, and text processing.

#3. Brand Impact

Today, deep learning is used to analyze a company’s brand exposure and with Brand Impact software, companies today can use deep neural networks to gain immediate and accurate results of their logo in the data they analyze. This not only allows organizations to gain the most accurate knowledge of brand impact but make their brand impact processes more effective as well.

As big data, artificial intelligence, machine learning, and deep learning continue to provide organizations with insights they need to achieve business goals and remain successful, it’s essential to be aware of the ways these technology advances are changing to be able to properly use them to our advantage.

If you want to learn more ways to accelerate deep learning with data, click the link below for more information.