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.

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. 

What is deep learning?

This subcategory of artificial intelligence and machine learning uses algorithms known as neural networks to “learn” and executive 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 one of three ways, through 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.

Read more: What Is Deep Learning? Definition, Examples, and Careers

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 high 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.

Read more: Deep Learning vs. Machine Learning: A Beginner’s Guide

10 deep learning applications

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 ten 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 benefitting from natural language processing.

Read more: What is Natural Language Processing? Definition and Examples

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.

Read more: What Is Facial Recognition?

6. Self-driving vehicles

Autonomous vehicles use deep learning to learn how to operate and handle different situations while driving, and it 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.

Getting started with Coursera

On Coursera, you can find highly rated courses on deep learning applications. For example, Neural Networks and Deep Learning from DeepLearning.AI will help you learn more about training and building neural networks, as well as how you can use deep learning to make applications.

Another option, Introduction to Deep Learning & Neural Networks with Keras from IBM, covers different deep learning models and helps you build your own deep learning models using the Keras library. 

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