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.
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.
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.
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.
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.
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
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.
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.
9. 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.
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.
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.