Approaches of Deep Learning : Part 4 (Conclusion)

This article will end our series on Approaches of Deep Learning which has total four parts – PART 1, PART 2, PART 3 and this current one. After reading so many thousands of words, the reader possibly have some theoretical idea around how to independently learn Deep Learning. This particular article in essence will draw conclusion on the topic.

 

Approaches of Deep Learning : Conclusion

 

Deep learning helps computers to derive meaningful links from a plethora of data and make sense of unstructured data. Here, the mathematical algorithms are combined with a lot of data and strong hardware to get qualified information. With this method, information from digital data can be automatically extracted, classified and analyzed.

Although deep learning has been around for several years, the trend has only really picked up in the last three to four years. The reason for this was among other things better hardware resources, more sophisticated algorithms and optimized neural networks. Deep learning is not a new approach but a development of the older approach of artificial neural networks.

Deep Learning can already be used today for different application scenarios:

1. Automatic speech recognition
2. Anaylysis of texts
3. Image recognition
4. Object recognition in an environment
5. Calculation of predictions

Deep Learning offers great potential for the automotive, medical and service industries, among the others. The economy also benefits from deep learning. More and more companies are using nVidia’s deep learning and hardware to analyze large amounts of data.
We never seen before machine learning or artificial intelligence technologies to have such a rapid impact on the economy unlike today. It is very impressive. Deep Learning is now mature enough for leading companies, such as Microsoft, to make their engines available to interested developers and businesses.

The greatest strength of Deep Learning is that it can learn on its own and react proactively to new information and situations without human intervention. This, in turn, enables companies to generate new product opportunities, resulting in improved solutions that increase customer value.

Computer moves away more and more from the model of the mathematically perfect machine, which shows small deviations at most, which can be modeled and compensated in advance with linear equations. Here the neural networks, which are trained for the respective robot, take the place of the prefabricated systems of equations. With deep learning, autonomous learning robots are not yet possible, but a household robot that can wipe stairs and stack dishes is now feasible.

Another way to use deep learning would be to equip smart glasses with chips that allow people to record and identify people unnoticed at any time using the built-in camera. For example, the captured image could be compared with Facebook, for example. But there are also laws that must be complied with. A great challenge for dealing with the possibilities of the new technology, especially with regard to the right to privacy.

Another example could be that the products in the supermarket no longer require a barcode. Everything that is put into the shopping cart is captured by a camera and automatically ruled out on the digital shopping list.
Deep Learning is on the way to growing up.

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