Deep Learning

Deep learning is a variety of machine learning algorithms that use multiple layers to gradually extract higher-level structures from raw input data. For example, in photo processing, the lower layers can help identify the edges. In contrast, the higher levels can be used to represent concepts that are ideal for humans, such as images, sounds, text, numbers, faces and letters.  
This technology is the most modern and advanced area of artificial intelligence, in which computers are programmed to perform various tasks through learning experience. Unlike classical or rule-based artificial intelligent systems, machine learning algorithms create behaviour through learning experience, i.e. by processing annotated examples.  
You can develop a fraud detection programme and have a Machine Learning Algorithm which contains a list of legitimate and illegitimate bank transactions and the corresponding results. The machine learning programme examines samples and detects unique characteristics between legitimate and illegitimate transactions.  
If you use the Algorithm feed it with data of new bank transactions, it will classify them as legitimate or illegitimate based on the examples already processed. However, the accuracy rate with which the machine performs its tasks depends on the quality of the data provided.  
More importantly, Deep Learning algorithms face similar problems when using deep neural networks. These neural networks are based on a unique software architecture that functions like the human brain.  
The ability to process incredible amounts of data and deliver high computing power makes this technology increasingly popular. In fact, it is the basis for many applications that we use every day. Such applications include, for example, online translation and automatic facial recognition on social media.  
This technology is important for driverless vehicles. It can be used to recognise and interpret traffic signs. It also enables voice control on mobile devices such as smartphones, tablets, hands-free devices and televisions.  
In the first quarter of 2019, computer scientists at MIT (Massachusetts Institute of Technology) used this technology to develop a new computer programme to detect breast cancer. So it has proven to be extremely useful in healthcare.  
Usually, this technology is crucial in problem solving when there are no well-defined or established rules. It is also effective when the basics cannot be coded to give unambiguous commands.   
Furthermore, different types of algorithms used work best for certain tasks. In summary, Deep Learning models are better suited to achieve the highest precision and accuracy, and sometimes even surpass human judgement. 

Data Navigator Newsletter