10 Deep Learning Best Applications

‍Introduction

Do artificial intelligence and machine learning ring a bell? These terms are rising in popularity in recent years as more and more technology seemingly revolves around them. Deep learning is less heard of but is an important part of the AI umbrella and a subfield of ML concerned with algorithms inspired by the structure and function of the brain. If you still don’t know what it does for the technological revolution, check out some of the best deep learning applications in today’s world.

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1. Virtual Assistants

“Alexa, play Despacito!” A command to Amazon’s virtual assistant soon turned into a widely-used joke. Today, people can order groceries, play music, control a smart Home, and call someone at the sound of their voice. In fact, Alexa, Siri, and Google Assistant are some of the most popular virtual assistants that reside in mini speakers or on your phone. When voicing your activation command, your assistant will begin to listen and respond accordingly. This is possible thanks to deep learning, as these technologies learn from natural human conversations to understand commands. As deep learning evolves, improving text generation and document summarization, you can even ask your virtual assistant to craft and send a simple email.

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2. Chatbots

Almost similar to virtual assistants, chatbots understand you but through chats. It is an AI application that has benefited businesses and consumers by providing timely FAQ answers, generating an automated response to user input, or marketing on social network sites. These chatbots use deep learning to generate different reactions based on what the customer has said, which is tremendously helpful when there are no human agents available, or it’s outside of working hours.

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3. Robotics & Healthcare

The field of robotics often garners attention as it makes breakthroughs with its technology. Deep learning has been used to build robots that perform human-like tasks for a while. It can be used to carry goods in hospitals, factories, warehouses, inventory, management, and more. The healthcare industry has hugely benefited from deep learning technology. Disease detection machines use deep learning to make more accurate detections. In medical research, it is also used for drug discovery and detecting cancer and other life-threatening diseases. Healthcare giants are mitigating health risks associated with readmission while lowering costs.

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4. Personalization

Do you notice how Youtube or Spotify recommendations are relatively in tune with what you want to see or hear? With deep learning, many platforms can now better personalize your experience by learning what you’re interested in and what you prefer to see based on what type of content you were interacting with the longest. Another eerie situation happened when a father was enraged when he noticed Target sending coupons for baby-related products to his teenage daughter. That’s because Target figured out the girl was pregnant through her purchasing patterns and made personalized recommendations.

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5. Self Driving Cars

This recent technology that has taken the world by storm uses deep learning algorithms to make safer autonomic choices on the road. It learns the driving laws and patterns and digests millions of scenarios to ensure safe driving. The next step with self-driving cars is to have them run on the road “map-less” and make choices based on whatever is available. This research is already in development with MIT.

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6. Natural Language Processing

Deep Learning is advancing rapidly in NLP, or Natural Language Processing. Robots learn and comprehend human language in this manner. Human language, on the other hand, is extremely difficult for machines to understand. They struggle to understand or generate it due to the letters, words, context, accents, handwriting, and other factors. Many of these issues are addressed by Deep Learning-based NLP, which trains computers (Autoencoders and Distributed Representation) to respond appropriately to language inputs.

7. Fraud Detection

Another appealing application for deep learning is fraud detection and prevention; big payment system companies are currently experimenting it. PayPal, for example, detects and prevents fraudulent behavior using predictive analytics technologies. The company claimed that evaluating user behavior sequences using neural networks' extended short-term memory architecture boosted anomaly detection by up to 10%. Sustainable fraud detection methods are critical for every fintech company, banking app, or insurance platform, as well as any organization that collects and uses sensitive data. Deep learning can make fraud more predictable and hence prevented.

8. Automated Handwriting Generation

In recent years, deep learning has transformed various fields. As a result of these improvements, the area of Machine Translation has shifted to the use of deep-learning neural-based methods, which have overtaken previous approaches like rule-based systems or statistical phrase-based methods. Because of vast amounts of training data and unequaled processing capacity, neural MT (NMT) models can now access the whole information accessible anywhere in the source phrase and automatically understand which piece is significant at which phase of synthesising the output text. The major cause of the considerable improvement in translation quality is the deletion of previous independence assumptions. As a consequence, neural translation was able to close the quality gap between human and neural translation.

9. Music Composing

A machine may learn music's notes, structures, and rhythms and begin generating music on its own. Raw audio may be created using Deep Learning-based generative models such as WaveNet.  The Long Short Term Memory Network aids in the automated generation of music. For computer-aided musicology, the Music21 Python toolkit is utilized. It enables us to educate a system to create music by teaching foundations of music theory, creating music samples, and researching music.

10. Advertising

In advertising, deep learning enhances the user experience. It assists publishers and advertisers in making their advertisements more relevant and effective. It also assists ad networks in cost-cutting by reducing the campaign cost per acquisition from $60 to $30. Deep Learning may be used to provide predictive advertising, real-time ad bidding, and customized display advertising.

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Conclusion

That was our list of the best applications using deep learning technology!

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It may come as a surprise to many how integrated these technologies are in our daily lives and may surprise a few more how deep learning will only continue to expand and take over a larger portion than it does now.

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At Dirox, we have teams of highly-skilled software developers with years of experience working with AI, ML, and DL projects. Get in touch with our expert consultants to start building your software product integrating deep learning!

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