10 Examples of Deep Learning Applications

Written by Coursera Staff • Updated on

Learn more about deep learning and examples of how deep learning applications are making an impact in different industries.

[Featured Image] A doctor sits at a desk in front of a computer and uses deep learning applications to diagnose diseases.

Key takeaways

Several examples of deep learning applications include fraud detection, customer service, financial services, and natural language processing.

  • Other deep learning applications include facial recognition, self-driving vehicles, predictive analytics, recommender systems, health care diagnoses, and industrial automation. 

  • Compared with machine learning algorithms, deep learning algorithms require less human assistance during training and are superior at processing unstructured data for unsupervised learning.

  • You can get started in deep learning by developing your IT and programming skills, earning a degree in computer science, working on practical projects, and joining a deep learning community. 

Discover more about deep learning applications, including different uses for this technology and how you can get started learning about it. If you’re ready to build your deep learning skills, enroll in the Deep Learning Specialization from DeepLearning.AI. In as little as three months, you can learn about artificial neural networks, large language modeling, natural language processing, TensorFlow, and more. 

What is deep learning?

This subcategory of artificial intelligence and machine learning uses algorithms known as neural networks to “learn” and execute its objectives. With deep learning, neural networks are trained by data sets, and over time, the neural network learns from features in the data. This process allows the neural network to make accurate predictions when analyzing data sets in the future. This training process typically happens in one of three ways: supervised, unsupervised, or reinforcement learning. 

With supervised learning, labeled training data helps train the neural network to identify specific features. In unsupervised learning, the neural network learns gradually without the help of labeled data. Reinforcement learning occurs through trial and error, receiving feedback to understand when it achieves the desired outcome.

Neural networks

The neural networks in deep learning are highly complex, with several layers of interconnected neurons. The typical structure of a neural network consists of three components, including the input, hidden, and output layers. The input layer receives data, and then moves on to the hidden layers for processing, where it analyzes data to identify patterns before moving to the output layer, where an output is ultimately delivered. The greater the number of hidden layers neural networks have, the more powerful they are.

Among the different types of neural networks in deep learning, two common ones include convolutional and recurrent neural networks. 

  • Convolutional neural networks can potentially have hundreds of hidden layers, each looking to identify specific features within data, often image data. These neural networks are a great option when dealing with complex and large quantities of data.

  • Recurrent neural networks effectively make predictions and can reuse output data as input data. This action makes recurrent neural networks excellent for use in speech recognition and forecasting the stock market since they consider historical information.

Deep learning vs. machine learning

Although deep learning and machine learning are similar, they have some key differences. Compared to machine learning algorithms, deep learning algorithms require less human assistance when training the model and are superior at processing unstructured data for unsupervised learning. This is because deep learning algorithms use larger data sets, giving them access to far more data points than machine learning models. However, this also leads to the training process in deep learning often taking much longer than training a machine learning algorithm.

What are the applications of deep learning? 

Deep learning applications have a broad reach, spanning various fields and industries, and are an important part of artificial intelligence. Deep learning even plays a role in some of the products and technologies you likely use daily. With a design inspired by the human brain, deep learning networks can process significant amounts of information, helping to make use of all the data that’s accessible in today's world so companies can improve their product offerings, learn more about consumers, and solve problems.

10 deep learning applications examples

Deep learning applications are making an impact across many different industries. You might even already use some of these applications in your everyday life. Let’s examine 10 examples highlighting deep learning’s broad use to understand it better. 

1. Fraud detection

Deep learning algorithms can identify security issues to help protect against fraud. For example, deep learning algorithms can detect suspicious attempts to log into your accounts and notify you, as well as inform you if your chosen password isn’t strong enough.

2. Customer service

You may have seen or used customer service help online and interacted with a chatbot to help answer your questions, or utilized a virtual assistant on your smartphone. Deep learning allows these systems to learn over time to respond.

3. Financial services

Several financial services can rely on assistance from deep learning. Predictive analytics helps support investment portfolios and trading assets in the stock market, as well as allowing banks to mitigate risk relating to loan approvals.

4. Natural language processing

Natural language processing is an important part of deep learning applications that rely on interpreting text and speech. Customer service chatbots, language translators, and sentiment analysis are all examples of applications benefiting from natural language processing.

5. Facial recognition 

An area of deep learning known as computer vision allows deep learning algorithms to recognize specific features in pictures and videos. With this technique, you can use deep learning for facial recognition, identifying you by your own unique features.

6. Self-driving vehicles

Autonomous vehicles use deep learning to learn how to operate and handle different situations while driving, and this allows vehicles to detect traffic lights, recognize signs, and avoid pedestrians. 

7. Predictive analytics

Deep learning models can analyze large amounts of historical information to make accurate predictions about the future. Predictive analytics helps businesses in several aspects, including forecasting revenue, product development, decision-making, and manufacturing. 

8. Recommender systems

Online services often use recommender systems with enhanced capabilities provided by deep learning models. With enough data, these deep learning models can predict the probabilities of certain interactions based on the history of previous interactions. Industries such as streaming services, e-commerce, and social media implement recommender systems.

9. Health care

Deep learning applications in the health care industry serve multiple purposes. Not only can they assist in developing treatment solutions, but deep learning algorithms are also capable of understanding medical images and helping doctors diagnose patients by detecting cancer cells.

Read more: What Is Machine Learning in Health Care?

10. Industrial

Deep learning applications in industrial automation help keep workers safe in factories by enabling machines to detect dangerous situations, such as when objects or people are too close to the machines.

Is ChatGPT deep learning?

ChatGPT, a large language model, uses deep learning to generate its outputs. When you input text into ChatGPT, it utilizes deep learning’s intelligence to comprehend relationships and patterns within that text and then produces new text that displays similar characteristics to your initial input. Machine learning engineers use large data sets to train deep learning models that ChatGPT relies on to generate content based on your prompts. 

How to get started with deep learning

Deep learning is a subset of machine learning with advanced capabilities to improve everyday functions through technology. If this exciting and emerging field interests you, consider the following structured approach to help you as you begin your deep learning journey.

1. Understand the basics.

It’s best to begin with the basics as you start your deep learning journey. Build a solid foundation in math and develop your IT and programming skills. Learn data structures and create an understanding of algorithms while working on general coding and programming. Familiarize yourself with probability and statistics, which are fundamental to understanding artificial intelligence, machine learning, and deep learning algorithms.

2. Study machine learning.

As deep learning is a subset of artificial intelligence and machine learning, it’s essential to have a solid understanding of relevant concepts, algorithms, and problem-solving techniques. You may find online courses, engage in projects, and get to grips with machine learning tools. Pursuing a bachelor's degree in machine learning, computer science, information technology, or a similar field would be beneficial if you haven't done so yet.

3. Explore deep learning.

As you move into deep learning, consider focusing on specialized learning. Delve into advanced topics, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). You might consider pursuing a master’s in machine learning or artificial intelligence.

4. Work on practical projects.

Get hands-on experience by applying your knowledge and working on projects. For instance, you can utilize public data sets from sources like Kaggle and the UC Irvine Machine Learning Repository. You can also use Google Dataset Search to practice and benchmark your models.

5. Join a community.

Engage with the deep learning community in person by attending meetings or conferences, or online via social media groups and forums. Doing so can provide support, inspiration, and opportunities for collaboration.

6. Prioritize continuous learning.

The deep learning field continues evolving. Stay updated by following leading researchers, attending research conferences, and keeping informed about the latest advancements and tools via workshops and online courses.

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