We previously learned about deep learning and how it works. Now what about its application in real, daily life? They are so well-integrated into products and services that users are unaware of the complex data processing that is taking place in the background. These are a few examples of deep learning applications across industries.
Deep learning algorithms can analyze and learn from transactional data to identify dangerous patterns that indicate possible fraudulent or criminal activity. Speech recognition, computer vision, and other deep learning applications can improve the efficiency and effectiveness of investigative analysis by extracting patterns and evidence from sound and video recordings, images, and documents. This helps law enforcement analyze large amounts of data more quickly and accurately.
Financial institutions regularly use predictive analytics to drive algorithmic trading of stocks, assess business risks for loan approvals, detect fraud, and help manage credit and investment portfolios for clients.
Many organizations incorporate deep learning technology into their customer service processes. Chatbots—used in various applications, services, and customer service portals—is a straightforward form of AI. Traditional chatbots use natural language and visual recognition, commonly found in call center-like menus. Based on the responses, the chatbot tries to answer these questions directly or route the conversation to a human user.
Virtual assistants like Apple’s Siri, Amazon Alexa, or Google Assistant extend the idea of a chatbot by enabling speech recognition functionality. This creates a new method to engage users in a personalized way.
The healthcare industry has significantly benefited from deep learning capabilities since digitizing hospital records and images. Image recognition applications can support medical imaging specialists and radiologists, helping them analyze and assess more images in less time. Speech disorders, autism, and developmental disorders can deny a good quality of life to children suffering from these problems. An early diagnosis and treatment can remarkably affect differently-abled children’s physical, mental, and emotional health. Hence, one of the noblest applications of deep learning is the early detection and course correction of these problems associated with infants and children.
Companies like Netflix, Amazon, YouTube, and Spotify give relevant movies, songs, and video recommendations to enhance customer experience. Netflix and Amazon are enhancing their deep learning capabilities to provide a personalized experience to their viewers by creating their personas factoring in show preferences, time of access, history, etc., to recommend shows that are of liking to a particular viewer. VEVO has been using deep learning to create the next generation of data services for personalized experiences for its users and subscribers and for artists, companies, record labels, and internal business groups to generate insights based on performance and popularity. Deep video analysis can save hours of manual effort required for audio/video sync and its testing, transcriptions, and tagging. Thanks to Deep Learning and its contribution to face and pattern recognition, content editing and auto-content creation are now a reality. Deep Learning AI is revolutionizing filmmaking as cameras learn to study human body language to imbibe in virtual characters.
Still, in the entertainment industry, a machine can learn the notes, structures, and patterns of music and start producing music independently. Deep Learning-based generative models such as WaveNet can be used to develop raw audio. Long Short-Term Memory Network helps to generate music automatically. Music21 Python toolkit is used for computer-aided musicology. It allows us to train a system to develop pieces by teaching music theory fundamentals, generating music samples, and studying music.