The era of technology and the use of data to make decisions are both here with us. Innovators can now rely on more advanced tools to collect, analyze and make better use of data. Deep learning is a concept that enables computers to be smarter so that they can work more like humans. Different deep learning methods are available for its learners.

For instance, a robot that performs different roles uses deep learning to make decisions. It is fed a vast amount of data and algorithms that are used to make decisions. Indeed, deep learning works, and it has proven results assisting humans in many ways including through conducting successful surgeries, operating vehicles autonomously and a lot more.

How Deep Learning Works

As mentioned, this is a technique where experts try to duplicate human cognitive power in machines. It is a sophisticated project, but the good news is that it has been successful so far. Driverless cars are an invention that is a product of this research. As we enjoy this technology, little do we know that there is much that happens in the background.

Significant computing power is necessary. Machines that are designed for deep learning have extremely powerful processors and other components that are very capable. As we are going to see, a lot of data is required to increase the accuracy of these machines. Their processing speed also must be very high if they will make decisions based on probability. In other words, gigabytes of data must be processed each second during active operation.

A large amount of data is needed. According to Active Wizards experts who help organizations with large data solutions, deep learning is currently the one single project that requires the highest amount of data. Even the smallest project like creating a surgery robot will require thousands of images, data sets and algorithm settings if it has to operate autonomously. The accuracy of deep learning, which goes beyond AI, relies on the amount of correct data that is fed into it.

Deep Learning Methods & Deep Learning in Operation

Now that we have seen how deep learning works, it is time to see practical examples of deep learning. So, we have already mentioned robotic surgery procedures and autonomous vehicles, which have been very successful. But there is a lot more that will surprise you. Take a look.

Industrial automation – with deep learning, certain areas of the human workforce are quickly being eliminated by automated operations. When deep learning is involved, it will not only increase productivity but also the safety of workers in many ways. Automotive industries have benefited a lot from deep learning because they can make many vehicles within a short time. It is right to conclude that any manufacturing company that has successfully automated their operations has taken up the use of deep learning. Their engineers can confirm that huge numbers of labelled data sets are used to make the operations happen.

Speech and text translation – it is through deep learning technology that translation services have become very successful. The involved software is fed with incredibly large amounts of data that enable the software to translate text and voice into many languages within a second. The precision in choosing the most accurate words depends on how much information is in the database and the algorithm technology that is used. Some software has been voted to be very accurate no matter the translation languages involved.

Social media – it is common for Facebook to automatically tag your friends when it recognizes their photos. Likewise, other social media platforms like Snapchat and Instagram also have facial recognition capabilities that are amazing. Behind the scenes, there is sophisticated technology that works seamlessly using deep learning. These photos are labeled and stored in databases. The technology can select them automatically without the intervention of a human and put them where they are needed.

Improving solar potential – Google has been on the front line in helping people find an optimal position for their solar panels. The project is called Google Sunroof. It uses Google Earth photos to make a 3D model of your roof and factors in things like weather conditions, trees and shadows from other buildings to obtain the optimal place and position for your solar panels. Deep learning is used to recognize trees, buildings and related weather conditions. Although home builders have been using this software heavily, many have yet to understand the technology behind it. Google engineers have been improving it, and they have promised even more functionalities in the future.

Autonomous vehicles – deep learning is the epicenter of success in autonomous vehicles. Although the project has been a success, according to tests carried out by BMW and Tesla among other automakers, it has yet to be rolled out completely. The vehicle computer is loaded with labeled images, text and voice recognition data to help it in making decisions. Therefore, the vehicle can operate without the help of any driver. In a few years, there will be many self-driving vehicles on the road thanks to deep learning.

Competitive Advantages of Deep Learning

There is no doubt that one of the main benefits of deep learning is achieving accurate and high-quality results. When the system is programmed to do something in a certain scenario, you can expect that this will always be the result, unlike in the case of human operation, where the outcome may change. Big factories have ensured their customers consistency in products, and deep learning helps in maintaining this. If you have a small company, you can also start with deep learning to make your products consistent.

The costs decrease by a large percentage when deep learning is involved. When operations are automated, the production per hour rises, and the resources used are fewer. Although the setup costs are high, you will start to enjoy low-cost benefits after some time; this will probably occur after a year of operation or slightly more.

Conclusion

Deep learning is a sophisticated technology that has been a major success within many deep learning methods. Many more sectors are also testing the viability of using this technology for increased accuracy and performance. If you are interested in learning more about deep learning or putting it into action, involve data professionals for more insights.

 

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