Top Deep Learning Applications Used Across Industries

Deep Learning is a part of Machine Learning used to solve complex problems and build intelligent solutions. The core concept of Deep Learning has been derived from the structure and function of the human brain. Deep Learning uses artificial neural networks to analyze data and make predictions. It has found its application in almost every sector of business. In this article, we’ll learn the top Deep Learning Applications.

Let’s begin exploring all the above Deep Learning Applications one by one.

Read more: Top Deep Learning Interview Questions and Answers for 2022

Deep Learning Applications

1. Virtual Assistants

Virtual Assistants are cloud-based applications that understand natural language voice commands and complete tasks for the user. Amazon Alexa, Cortana, Siri, and Google Assistant are typical examples of virtual assistants. They need internet-connected devices to work with their full capabilities. Each time a command is fed to the assistant, they tend to provide a better user experience based on past experiences using Deep Learning algorithms.

Deep Learning Course (with TensorFlow & Keras)

Master the Deep Learning Concepts and ModelsView Course
Deep Learning Course (with TensorFlow & Keras)

2. Chatbots

Chatbots can solve customer problems in seconds. A chatbot is an AI application to chat online via text or text-to-speech. It is capable of communicating and performing actions similar to a human. Chatbots are used a lot in customer interaction, marketing on social network sites, and instant messaging the client. It delivers automated responses to user inputs. It uses machine learning and deep learning algorithms to generate different types of reactions.

The next important deep learning application is related to Healthcare.

3. Healthcare

Deep Learning has found its application in the Healthcare sector. Computer-aided disease detection and computer-aided diagnosis have been possible using Deep Learning. It is widely used for medical research, drug discovery, and diagnosis of life-threatening diseases such as cancer and diabetic retinopathy through the process of medical imaging.

4. Entertainment

Deep Learning Application - Entertainment

Companies such as Netflix, Amazon, YouTube, and Spotify give relevant movies, songs, and video recommendations to enhance their customer experience. This is all thanks to Deep Learning. Based on a person’s browsing history, interest, and behavior, online streaming companies give suggestions to help them make product and service choices. Deep learning techniques are also used to add sound to silent movies and generate subtitles automatically.

Next, we have News Aggregation as our next important deep learning application.

Free Deep Learning for Beginners Course

Master the Basics of Deep LearningEnroll Now
Free Deep Learning for Beginners Course

5. News Aggregation and Fake News Detection

Deep Learning allows you to customize news depending on the readers’ persona. You can aggregate and filter out news information as per social, geographical, and economic parameters and the individual preferences of a reader. Neural Networks help develop classifiers that can detect fake and biased news and remove it from your feed. They also warn you of possible privacy breaches.

6. Composing Music

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 music by teaching music theory fundamentals, generating music samples, and studying music.

Next in the list of deep learning applications, we have Image Coloring.

7. Image Coloring

Deep Learning Application - Image Coloring

Image colorization has seen significant advancements using Deep Learning. Image colorization is taking an input of a grayscale image and then producing an output of a colorized image. ChromaGAN is an example of a picture colorization model. A generative network is framed in an adversarial model that learns to colorize by incorporating a perceptual and semantic understanding of both class distributions and color.

8. Robotics

Deep Learning is heavily used for building robots to perform human-like tasks. Robots powered by Deep Learning use real-time updates to sense obstacles in their path and pre-plan their journey instantly. It can be used to carry goods in hospitals, factories, warehouses, inventory management, manufacturing products, etc. 

Boston Dynamics robots react to people when someone pushes them around, they can unload a dishwasher, get up when they fall, and do other tasks as well.

Now, let’s understand our next deep learning application, i.e. Image captioning.

FREE Machine Learning Course

Learn In-demand Machine Learning Skills and ToolsStart Learning
FREE Machine Learning Course

9. Image Captioning

Deep Learning Application - Image Captioning

Image Captioning is the method of generating a textual description of an image. It uses computer vision to understand the image's content and a language model to turn the understanding of the image into words in the right order. A recurrent neural network such as an LSTM is used to turn the labels into a coherent sentence. Microsoft has built its caption bot where you can upload an image or the URL of any image, and it will display the textual description of the image. Another such application that suggests a perfect caption and best hashtags for a picture is Caption AI.

10. Advertising

In Advertising, Deep Learning allows optimizing a user's experience. Deep Learning helps publishers and advertisers to increase the significance of the ads and boosts the advertising campaigns. It will enable ad networks to reduce costs by dropping the cost per acquisition of a campaign from $60 to $30. You can create data-driven predictive advertising, real-time bidding of ads, and target display advertising.

Read more: Differences Between Machine Learning and Deep Learning

11. Self Driving Cars

Deep Learning is the driving force behind the notion of self-driving automobiles that are autonomous. Deep Learning technologies are actually "learning machines" that learn how to act and respond using millions of data sets and training. To diversify its business infrastructure, Uber Artificial Intelligence laboratories are powering additional autonomous cars and developing self-driving cars for on-demand food delivery. Amazon, on the other hand, has delivered their merchandise using drones in select areas of the globe. 

The perplexing problem about self-driving vehicles that the bulk of its designers are addressing is subjecting self-driving cars to a variety of scenarios to assure safe driving. They have operational sensors for calculating adjacent objects. Furthermore, they manoeuvre through traffic using data from its camera, sensors, geo-mapping, and sophisticated models. Tesla is one popular example.

PCP in AI and Machine Learning

In Partnership with Purdue UniversityExplore Course
PCP in AI and Machine Learning

12. Natural Language Processing

Another important field where Deep Learning is showing promising results is NLP, or Natural Language Processing. It is the procedure for allowing robots to study and comprehend human language. 

However, keep in mind that human language is excruciatingly difficult for robots to understand. Machines are discouraged from correctly comprehending or creating human language not only because of the alphabet and words, but also because of context, accents, handwriting, and other factors. 

Many of the challenges associated with comprehending human language are being addressed by Deep Learning-based NLP by teaching computers (Autoencoders and Distributed Representation) to provide suitable responses to linguistic inputs.

13. Visual Recognition

Just assume you're going through your old memories or photographs. You may choose to print some of these. In the lack of metadata, the only method to achieve this was through physical labour. The most you could do was order them by date, but downloaded photographs occasionally lack that metadata. Deep Learning, on the other hand, has made the job easier. Images may be sorted using it based on places recognised in pictures, faces, a mix of individuals, events, dates, and so on. To detect aspects when searching for a certain photo in a library, state-of-the-art visual recognition algorithms with various levels from basic to advanced are required. 

14. Fraud Detection

Another attractive application for deep learning is fraud protection and detection; major companies in the payment system sector are already experimenting with it. PayPal, for example, uses predictive analytics technology to detect and prevent fraudulent activity. The business claimed that examining sequences of user behaviour using neural networks' long short-term memory architecture increased anomaly identification by up to 10%. Sustainable fraud detection techniques are essential for every fintech firm, banking app, or insurance platform, as well as any organisation that gathers and uses sensitive data. Deep learning has the ability to make fraud more predictable and hence avoidable.

Free Course: Machine Learning Algorithms

Learn the Basics of Machine Learning AlgorithmsEnroll Now
Free Course: Machine Learning Algorithms

15. Personalisations

Every platform is now attempting to leverage chatbots to create tailored experiences with a human touch for its users. Deep Learning is assisting e-commerce behemoths such as Amazon, E-Bay, and Alibaba in providing smooth tailored experiences such as product suggestions, customised packaging and discounts, and spotting huge income potential during the holiday season. Even in newer markets, reconnaissance is accomplished by providing goods, offers, or plans that are more likely to appeal to human psychology and contribute to growth in micro markets. Online self-service solutions are on the increase, and dependable procedures are bringing services to the internet that were previously only physically available.

16. Detecting Developmental Delay in Children

Early diagnosis of developmental impairments in children is critical since early intervention improves children's prognoses. Meanwhile, a growing body of research suggests a link between developmental impairment and motor competence, therefore motor skill is taken into account in the early diagnosis of developmental disability. However, because of the lack of professionals and time restrictions, testing motor skills in the diagnosis of the developmental problem is typically done through informal questionnaires or surveys to parents. This is progressively becoming achievable with deep learning technologies. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory and the Institute of Health Professions at Massachusetts General Hospital have created a computer system that can detect language and speech impairments even before kindergarten.

17. Colourisation of Black and White images

The technique of taking grayscale photos in the form of input and creating colourized images for output that represent the semantic colours and tones of the input is known as image colourization. Given the intricacy of the work, this technique was traditionally done by hand using human labour. However, using today's Deep Learning Technology, it is now applied to objects and their context inside the shot - in order to colour the image, in the same way that a human operator would. In order to reproduce the picture with the addition of color, high-quality convolutional neural networks are utilized in supervised layers.

18. Adding Sounds to Silent Movies

In order to make a picture feel more genuine, sound effects that were not captured during production are frequently added. This is referred to as "Foley." Deep learning was used by researchers at the University of Texas to automate this procedure. They trained a neural network on 12 well-known film incidents in which filmmakers commonly used Foley effects. Their neural network identifies the sound to be generated, and they also have a sequential network that produces the sound. They employed neural networks to transition from temporally matched visuals to sound creation, a completely another medium!

Artificial Intelligence Engineer

Your Gateway to Becoming a Successful AI ExpertView Course
Artificial Intelligence Engineer

19. Automatic Machine Translation

Deep learning has changed several disciplines in recent years. In response to these advancements, the field of Machine Translation has switched to the use of deep-learning neural-based methods, which have supplanted older approaches such as rule-based systems or statistical phrase-based methods. Neural MT (NMT) models can now access the whole information accessible anywhere in the source phrase and automatically learn which piece is important at which step of synthesising the output text, thanks to massive quantities of training data and unparalleled processing power. The elimination of previous independence assumptions is the primary cause for the remarkable improvement in translation quality. This resulted in neural translation closing the quality gap between human and neural translation.

20. Automatic Handwriting Generation

This Deep Learning application includes the creation of a new set of handwriting for a given corpus of a word or phrase. The handwriting is effectively presented as a series of coordinates utilised by a pen to make the samples. The link between pen movement and letter formation is discovered, and additional instances are developed.

21. Automatic Game Playing

A corpus of text is learned here, and fresh text has created word for word or character for character. Using deep learning algorithms, it is possible to learn how to spell, punctuate, and even identify the style of the text in corpus phrases. Large recurrent neural networks are typically employed to learn text production from objects in sequences of input strings. However, LSTM recurrent neural networks have lately shown remarkable success in this challenge by employing a character-based model that creates one character at a time.

22. Language Translations

Machine translation is receiving a lot of attention from technology businesses. This investment, along with recent advances in deep learning, has resulted in significant increases in translation quality. According to Google, transitioning to deep learning resulted in a 60% boost in translation accuracy over the prior phrase-based strategy employed in Google Translate. Google and Microsoft can now translate over 100 different languages with near-human accuracy in several of them.

23. Pixel Restoration

It was impossible to zoom into movies beyond their actual resolution until Deep Learning came along. Researchers at Google Brain created a Deep Learning network in 2017 to take very low-quality photos of faces and guess the person's face from them. Known as Pixel Recursive Super Resolution, this approach uses pixels to achieve super resolution. It dramatically improves photo resolution, highlighting salient characteristics just enough for personality recognition.

24. Demographic and Election Predictions

Gebru et al used 50 million Google Street View pictures to see what a Deep Learning network might accomplish with them. As usual, the outcomes were amazing. The computer learned to detect and pinpoint automobiles and their specs. It was able to identify approximately 22 million automobiles, as well as their make, model, body style, and year. The explorations did not end there, inspired by the success story of these Deep Learning capabilities. The algorithm was shown to be capable of estimating the demographics of each location based just on the automobile makeup.

FREE Course: Intro to AI

Learn the Core AI Concepts and Key SkillsStart Learning
FREE Course: Intro to AI

25. Deep Dreaming

DeepDream is an experiment that visualises neural network taught patterns. DeepDream, like a toddler watching clouds and attempting to decipher random forms, over-interprets and intensifies the patterns it finds in a picture.

It accomplishes this by sending an image across the network and then calculating the gradient of the picture in relation to the activations of a certain layer. The image is then altered to amplify these activations, improving the patterns perceived by the network and producing a dream-like visual. This method was named "Inceptionism" (a reference to InceptionNet, and the movie Inception).

Stay ahead of the tech-game with our PG Program in AI and Machine Learning in partnership with Purdue and in collaboration with IBM. Explore more!

Conclusion

Deep Learning has found its prominence in almost every sector of business. It is being used in E-Commerce, Healthcare, Advertising, Manufacturing, Entertainment, and many other industries.

Deep Learning has revolutionized our lives by making tasks easier. Do you have any questions related to this article on Deep Learning Applications? Do you think we missed out on any essential applications? Then, please put your queries/inputs in the comments section. We'll be happy to help you. To start your Deep Learning career, click on the following link: AI and Machine Learning certification courses.

About the Author

Avijeet BiswalAvijeet Biswal

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.