Has Deep Learning caused severe disruption to all existing technology?

                                         

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      The conventional machine learning or other technology fails to deal with unstructured data. Now the reality is that in this world the majority of the data is unstructured. The second important point is that the feature extraction of the data. In both these two cases, deep learning has resulted positively. Deep learning which is an advanced version of machine learning has grabbed the whole academia and industries in recent times and is far ahead in terms of feature extraction especially in terms of extracting the information from unstructured data such as image data. It can detect features that are nonlinear by nature and it is capable of making a relationship among arbitrary variables by approximating them using arbitrary functions. In the case of feature learning, deep learning is far away from others. The variant of deep learning architectures such as deep neural networks(DNN), deep belief networks(DBN), recurrent neural networks(RNN) and convolutional neural networks(CNNs) have been exploited to the arena of computer vision, speech recognition, NLP, sound classification and audio data recognition since five years. Therefore it has a huge industry impact which has been very minutely being noticed by the industry experts as well. Starting from developing high-performance workstations, various security products, computer vision, driverless cars, high-level image classification and segmentation, robotics, to the many other images related problems and especially data imbalance problems, deep learning's potential capability has always resulted positively. The term disruption first coined by the American scholar Clayton M. Christensen and his collaborators in the early stage of 1995. It is believed that disruptive technology has far impact and will dominate the era of the 21st century.

What is a disruptive technology?

From a business perspective, the theory of disruption in terms of innovation is the technology that builds a fresh market and adds value to society and by this process, people used to give up the existing technology that they were using. In a simple language, disruptive technology replaces the current one and changes the existing product's fate and eventually deletes the current product from the marker which causes the heavy price to the owners of the existing companies. So everything related to the old technology vanishes such as employees, alliances, product design protocols, customers, and revenues.

Why Deep Learning can disrupt the existing ones?

Below, I have jotted down a few points that could be the reasons why deep learning is a disruptive technology.

1. Deep learning can provide a solid feature engineering platform even with the huge amount of images or video related data; that means deep learning can provide a solution for classification or critical segmentation to the image-related problems.
2. It can handle unstructured data with a fair amount of efficiency.
3. The data labeling becomes immaterial to deep learning as it can apply unsupervised algorithms as well.
4. Feature engineering becomes easier now. Thanks to deep learning.
5. With the use of high-end processors such as GPU/TPU, alongside the easy availability of cloud were executing a fast algorithm is no big deal nowadays, deep learning has surely produced very good results.
6. Now no fear of data imbalance phenomenon. Data augmentation and other preprocessing techniques perfectly can be incorporated with a deep learning paradigm.
7. Python, Keras, and TensorFlow framework perfectly provide a quality framework to develop any efficient model for high-level feature engineering problems.
8. Earlier the available  packages for deep learning implementations on python or in R was bit difficult, now with the newly developed PyTorch, Tensorflow and Keras framework of deep learning algorithms such as DNN, CNN, RNN, and Gan, the execution has become much easier.
9. The difficulty with the processing power has also sort of vanishes, especially with the availability of free google colab.
10. Google colab which is a Jupyter alike notebook has reduced considerably the processing time and the dynamics of the whole programming environment. Things are much handy and attractive now.

    Read some other interesting articles on these following  
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7. Top three books on machine learning and deep learning.

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~AISavvy,2020