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Updated: Aug 23, 2021

Real-world deep learning apps are part of our daily lives, but in many cases they are so well integrated into products and services that users are unaware of the complex data processing that takes place in the background. Some of these examples include:

Law enforcement

Deep learning algorithms can analyze and learn from transaction data to identify dangerous patterns indicating possible fraud or criminal activity. Speech recognition, computer vision, deep learning, and other applications, which helps to analyze large amounts of data more quickly and accurately law enforcement audio and video recordings, images and documents from the analysis of phrases and can increase the efficiency and effectiveness of the investigation by the evidence.

Financial services

Financial institutions, algorithmic shares trade route for business loan approvals assess risks, detect fraud, and customers to help manage their loan and investment portfolios predictive analytics uses on a regular basis.

Customer service

Many organizations incorporate deep learning technology into their customer service processes. Chatbots, used in various applications, services and customer service portals, are a simple form of artificial intelligence. Traditional chatbots use natural language and even visual recognition, often found in call center-like menus. However, more complex chatbot solutions attempt to determine through learning whether there are multiple answers to ambiguous questions. The Chatbot tries to answer these questions directly or direct the conversation to a human user based on the answers it receives.

Virtual assistants such as Apple Siri, Amazon Alexa or Google Assistant are expanding the idea of a chat robot by enabling speech recognition. This creates a new method for engaging users in a personalized way.


The health sector has benefited greatly from deep learning capabilities since the digitisation of hospital records and images. Image recognition apps can help them analyze and evaluate more images in less time by supporting medical imaging specialists and radiologists.

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