Our Data Scientist Mukhamet Nurpeiisov has provided a workshop for the National Guard of the Republic of Kazakhstan on the topic of Deep Learning.

The workshop consisted of an overall overview of deep learning and its use cases. In addition Mukhamet provided a tutorial for creating face detection deep learning model. He showed how to create a dataset and how important it is for the work. Furthermore, they have trained the face detection model and tested it. 

Deep Learning is the sub-field in machine learning. Inspired by biological processes of the human brain, artificial neural networks (ANN) were developed.  “Deep learning” refers to these artificial neural networks that are composed of many layers. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text. It’s called “Deep” because of the structure of those ANNs as it is common to have neural networks with 10+ layers. Using multiple levels of neural networks in deep learning, computers now have the capacity to see, learn, and react to complex situations like human brains or even better.

Deep Learning is now used in areas like natural language processing, computer vision and pattern recognition. Many of the recent advances in deep learning have occurred in these areas. To name a few: “Google Translate” is using deep learning and image recognition to translate not only voice but written languages as well; All voice assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition; Amazon, Netflix & Spotify are using recommendation engines using deep learning for the next best offers, movies or music.

So Deep Learning has so far aided image classification, language translation, speech recognition, pattern recognition problem-solving, and all of it is happening without human intervention. This is without a doubt a disruptive digital technology that is being used by more and more companies to create new business models.