Deep learning applications are laying the foundation of business decisions.
Deep learning, a subset of artificial intelligence, is already making its way into day-to-day aspects of life and business. A few years back, the technology was touted to be the futuristic concept as it differs from traditional machine learning systems. Today, deep learning is capable of self-learning and improving as it assesses large data sets. It has a large number of business applications and has the potential to revolutionize industries, emerging as the next big disruption of AI.
Deep learning is typically designed to imitate the way the human brain processes data. It re-creates the patterns found in the brain’s decision-making process. This technology enables computers to solve complex problems. As deep learning has become more and more widespread over the years, its applications are powering a wide range of industries, everything from autonomous driving to content recommendations and object classification to medicine.
Let’s have a look at the top deep learning applications across industries and how they are founding themselves in becoming mainstream.
The human brain easily encounters distinct entities of the visual world and distinguishes objects with ease. In contradiction of human brains, deep learning models view visuals as an array of numerical values and finds patterns in the digital image, it can be a still, video, graphic, or even live, in order to identify and differentiate key features of the image. This technology is highly germane for things related to computer vision, which trains computers to gain high-level understanding from digital images or videos. Social networking giant Facebook, for instance, has more enough data on images to work with, making deep learning for image detection much accessible.
Almost every industry in today’s digital age is susceptible to anomaly and criminal activities. Frauds do not involve any constant pattern that is making it complex to detect and predict in advance. Using deep learning models can be effective to spot system vulnerabilities and suspicious behavior in customer accounts. Unlike traditional fraud detection systems that were often limited, deep learning-powered systems can offer companies a more adaptable, comprehensive system. Fraud detection is typically done on recognizing patterns in customer transactions and credit scores that help classify suspicious behaviors and outliers.
Customer Relationship Management
Making any communication with clients can be taken as a vital data source that can be then used for evaluating their behavior, dealing with issues, changing process approach and operations, and eventually transforming the customer journey. Integrating deep learning models for CRM can enable companies to streamline, manage, and analyze customer interactions. Burberry, a luxury fashion retailer, for instance, used deep learning and big data to reinvent its entire business model. Using deep learning models enabled the company to establish deeper connections with its customers, allowing it to deliver personalized recommendations, both online and in-store.
Application of deep learning can also be found in automated translations of image-language. Using the Google Translate app, it has become possible to automatically translate photographic images with text into a real-time language of a user choice. In recent years, deep learning methods have made significant progress and quickly become the new de facto paradigm of machine translation in both academia and industry. This, in the coming days, will obviously become a useful application considering languages and allowing universal human communication.
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