Deep Learning (DL) is a part of Machine Learning (ML) and is primarily used to solve complex problems and to build intelligent solutions. While ML is a broad term and consists of several techniques, DL leverages neural networks to analyze huge volumes of data, discover patterns, and make accurate decisions.
Today, deep learning is actively being used in different fields such as healthcare, finance, cybersecurity, and more. As a result, it is rapidly becoming the central technology on which several business functions depend.
In this article, we explain what is deep learning and why is it used and describe the top 20 deep learning applications such as natural language processing, fraud detection, language translation, and more.
Also read: Is the future all about data science and machine learning?
What is Deep Learning and Why is it Used?
Deep learning is an AI-based method that focuses on teaching computers to process data like humans. It is capable of identifying complex patterns in images, texts, sounds, and other data and can produce accurate insights and predictions.
It uses multi-layered neural networks, also known as deep neural networks to help make complex decisions and simulate the power of a human brain.
Deep learning is used in many fields today as it attempts to train computers and machines to think like humans. It is used in applications like digital assistants, fraud detection, and more.
The main reason for using deep learning is that it helps perform complex tasks, thereby simplifying many processes and also aiding quick decision-making. â€
Deep Learning Applications in 2024
To understand what are the various deep learning applications, think of a road with no accidents or think of a world where every surgery is successful and there are no surgical errors.
If this seems far-fetched, then think of a time when you can segregate your old images according to the parameters and criteria you set such as date, time, location, etc. This is all possible with the use of deep learning.
Now, let’s look into the top 20 deep-learning applications:
1. Fraud detection
One of the key industries focusing on deep learning is the finance and banking industry. This sector is using DL to detect frauds as a majority of transactions are now being made through digital mediums.
Several tools and techniques have been developed using deep learning to detect credit and debit card frauds saving billions of dollars for the industry. It identifies patterns in customer transactions & credit scores, and many other such factors to determine any fraudulent activity.
2. Natural Language Processing (NLP)
Another prominent deep learning application is NLP, where it is being used to understand the complexities of language such as the use of syntax, tonal nuances, sarcasm, etc. Identifying and understanding these is by far one of the most difficult tasks for humans.
Deep learning is being used in NLP to train machines to catch linguistic nuances and give appropriate responses. Deep learning is being used to train machines to answer questions, language modeling, etc to improve NLP.
3. Healthcare
Healthcare is another major industry where the use of Deep Learning has increased significantly in recent years. Its techniques are being used to make quick and early diagnosis of life-threatening diseases.
The use of deep learning is helping minimize the burden on clinicians and doctors and addressing the lack of quality physicians & healthcare providers.
It is also being used in clinical research to increase their speed and make them even more accurate in finding effective cures for untreatable diseases.
Also read: Deep Learning Fundamentals: An Introduction to Neural Networks
4. Automatic Handwriting Generation
This is another deep-learning application. It includes generating new handwriting for a given set of words or phrases. DL generates new handwriting in the form of a sequence of coordinates that will be used by a pen to create the samples.
The machines learn this relationship between the movement of the pen and the letters formed to generate new handwriting examples.
5. Automatic Machine Translation
Deep learning uses convolutional neural networks to identify images with visible letters so that they can be turned into text, translated, and recreated with an image that uses this translated text.
It is known as Instant Visual Translation and this process involves automatic translations into other languages with a given set of words, phrases, or sentences.
Automatic machine translation is achieving great results in two main areas – automatic text translation and automatic image translation. The text translation is performed without any pre-processing of the sequences.
6. Pixel Restoration
Another deep learning application is restoring pixels in videos and photos. It can now take very low-resolution images of faces and identify the person. This method helps enhance resolution significantly, allowing pinpointing of key features to identify a person.
7. Language Translations
In recent years, machine translation has received a lot of attention from the technology business.
This investment in deep learning has significantly helped in increasing the quality of translations. With the help of DL, it is now possible to translate photographic images that have text into a language of your choice that can be read and interpreted easily.
This application of deep learning is helping eliminate language barriers, allowing for universal human communication.
8. Photo Descriptions
Computers have the capability to classify photos, however, the emergence of deep learning has made it possible to describe what is there in the photo. It can describe every individual element in the image.
Deep learning was trained to identify several interesting areas in an image and write 1-2 sentences each to describe them. Based on this, it can be said that computers can now not only classify the images, but they can also describe each element in perfect English grammar.
9. Deep Dreaming
Deep learning networks are now being used to enhance the features in images stored on computers. However, one evolved use is deep dreaming which allows the computer to hallucinate on top of an existing photo.
This generates a reassembled dream while the hallucination varies depending on the type of neural network. This deep learning technique has helped researchers develop a hallucination machine that allows users to experience psycho-pathological conditions through virtual reality.
10. Virtual Assistants
Siri and Alexa are the most popular examples of deep learning applications where machines and software act as virtual assistants and carry out the tasks given to them. They learn from every interaction.
The virtual assistants use deep learning to obtain more information about their subjects such as movie preferences, sleep timing, etc. By using DL techniques they evaluate the natural human language and learn to understand instructions given to them by humans.
11. Chatbots
Chatbots are a very common example of the use of deep learning. These can solve customer’s problems in a fraction of a second. It can interact with the customers through chats and even perform actions similar to humans.
Today, chatbots are used in different industries to improve customer interaction, marketing on social networking sites, and more. It helps deliver automated responses to the customers based on their inputs.
It uses machine learning algorithms to generate different reactions and responses.
12. Entertainment
Deep learning is also being used in the entertainment industry. Platforms like Netflix are using this technology to give personalized recommendations to the users.
They are improving their deep learning capabilities to improve the experience of the users and give them recommendations based on their likes & dislikes and watch history.
It is also being used by artists, record labels, and others to understand the behavior of the audiences based on their performance and popularity.
13. Demographic and Election Predictions
Deep learning is also being used to detect automobiles and their specifications. Technology companies and governments are using DL to perform such actions to better understand the target audiences.
Such technology can also be used as a means to predict election results. It can not only be used to see the demographics of an area just from the makeup of the car.
14. Robotics
The field of robotics is heavily dependent on deep learning to develop robots that are capable of performing tasks like a human.
Such robots are powered by deep learning and use real-time updates to sense barriers and obstacles in their path and modify their path accordingly.
The use of deep learning in robotics can help build robots that can be used to carry things in a hospital or a factory and many more such places.
15. Adding Sound to Silent Movies
Deep learning is also being used to add sound to silent movies. This is done by associating the video frames with a database of pre-recorded sounds and selecting the sound that matches well with the scene.
To perform this task, 1000s of videos trained by DL are used. The deep learning models predict the best-suited sound to the video and later test if the sound is real or fake.
16. Automatic Game Playing
This deep learning application is capable of learning how to spell, punctuate, and capture the style of text present in the group of sentences.
In this deep learning model, usually, large recurrent neural networks are used so that the machines can learn text generation through the items present in the string of sentences as input.
17. Colorization of Black and White Images
Image colorization is the process of taking grayscale images as input and producing colorized images as output representing the semantic colors and tones of the input. Earlier, this process was done by hand and thus required significant human efforts.
But, with the emergence of deep learning, the majority of this task is being done by AI and machines. It involves the use of high-quality convolutional neural networks to recreate the image with the addition of appropriate colors.
18. Visual Recognition
Another key deep learning application is visual recognition. You might have a plethora of old images that might take you down memory lane.
You might want to frame some of those pictures. But the only way you can do so is by sorting out the images manually based on information such as location, event, etc.
However, this becomes difficult in the absence of metadata, which usually the downloaded images lack. Here the role of deep learning becomes crucial.
With the help of this technology you can sort the photos on metadata such as location, faces in the photographs, events, dates, etc. The use of deep learning in visual recognition is increasing rapidly in the digital media management industry.
19. Self-Driving Cars
Deep learning is playing a vital role in turning autonomous driving into a reality. A large amount of data sets are being fed to systems to build models, train the machines to learn, and test the results.
One of the major concerns for driverless car developers is handling unforeseen situations. Such vehicles are being fitted with operational sensors that can identify any nearby objects. Training the machines and cars with such data is the prime responsibility of deep learning.
20. Weather Forecasting
Today deep learning is being used to predict weather conditions. It has greatly helped in improving the accuracy of weather forecasts. With such technology, you can view time-stamped aerial images of different locations.
This information can be used to figure out ways to prepare an area if extreme weather conditions occur. Similarly, deep learning can also be used to detect extreme weather climate patterns.
FAQs: Deep Learning Applications
How does deep learning differ from traditional machine learning?
While both deep learning and traditional machine learning fall under the umbrella of artificial intelligence, deep learning specifically uses neural networks with multiple layers (hence the term “deep”) to process data and learn from it.
Traditional machine learning might use simpler algorithms and often requires more manual feature extraction.
Can deep learning be used for personalized marketing?
Yes, deep learning is increasingly used for personalized marketing.
By analyzing customer data and behavior patterns, deep learning algorithms can provide highly personalized recommendations and targeted advertisements, enhancing customer engagement and conversion rates.
What role does deep learning play in cybersecurity?
In cybersecurity, deep learning is used to detect and prevent threats by analyzing patterns in network traffic, identifying anomalies, and predicting potential security breaches.
It enhances the ability to detect sophisticated cyber-attacks that traditional methods might miss.
How is deep learning used in the field of education?
Deep learning is transforming education through applications such as personalized learning, where algorithms adapt to individual students’ learning styles and paces.
It is also used in automated grading, providing real-time feedback, and developing intelligent tutoring systems that can interact with students more naturally.
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