What Are Deep Learning Algorithms?

Written by Coursera Staff • Updated on

Deep learning algorithms are at the forefront of artificial intelligence. Learn more about deep learning algorithms, discover how they work, and take a look at unsupervised deep learning algorithms.

[Featured image] A woman in a cybersecurity boot camp learns about deep learning algorithms.

Chatbots, media recommendation features, social media feeds, and predictive text are all examples of machine learning, a subset of artificial intelligence that allows computer systems to learn without humans. As machine learning has grown more advanced, it’s given rise to deep learning, or the ability of a machine to learn automatically without any human intervention. Deep learning algorithms mimic the human brain and often provide the experience of interacting with an actual human.

Read more about deep learning algorithms, including their benefits and uses, and understand how unsupervised deep learning algorithms work.

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

What are deep learning algorithms?

Also known as deep learning neural networks, these computer programs mimic the human brain’s structure. They contain multiple layers of nodes that may connect with others in layers above and below, allowing them to send and receive data. These interconnected nodes allow the algorithm to recognize, describe, and sort data. Deep learning algorithms are vital for automating complex tasks that typically require human intelligence to complete successfully, such as language translation.

Deep learning algorithms are unique because they can learn independently and self-correct, quickly increasing their effectiveness and accuracy. The multiple layers of artificial neurons within a deep learning algorithm mean it can perform several mathematical translations in a row to help reach more accurate or insightful conclusions.

Unsupervised deep learning algorithms work with untagged data. The computer analyzes these data sets and creates its own rules and patterns for sorting and extrapolating results from the data. This method allows the computer to learn without human intervention and grow increasingly accurate over time.

Read more: What Are Deep Learning Models?

What are deep learning algorithms used for?

Deep learning algorithms work within complex tasks to process data and power artificial intelligence (AI) applications for use in a wide range of fields, including manufacturing, health care, aerospace engineering, technology, and automotive. Some examples include the following. 

1. Customer service

Chatbots and virtual assistants are examples of technology employing deep learning algorithms. They can comprehend typed or spoken questions and create appropriate answers. Deep learning allows these answers to be unique to each question rather than rote responses selected from a bank.

2. Health care

In health care, deep learning algorithms achieve tasks as complex as identifying and diagnosing cancer cells or predicting patient no-shows based on local weather reports. The ability of a deep learning algorithm to interact with visual images is critical for supporting health care professionals within medical imaging.

3. Self-driving cars

Deep learning algorithms allow autonomous vehicles to learn how to drive safely while on the road. The cars “watch” other vehicles as they move, then mimic safe driving techniques and use the information to inform their own decisions. In one study, this ability to learn from driving data helped self-driving cars to safely reach their destinations more than 96 percent of the time [1].

How do deep learning algorithms work?

Layers of interconnected nodes allow deep learning algorithms to process information and supply insights and answers like the human brain does. Each node handles one small part of the data or problem, and then its solutions pass to the next layer of nodes to create complex and nuanced results. Deep learning algorithms have three components:

  • The input layer has nodes that intake raw data.

  • The hidden layers process information at different levels, changing their behavior based on each level's insights.

  • The output layer synthesizes the results and reports them.

For example, you might use deep learning algorithms to help identify financial fraud in a banking system. To do so, you’d provide an algorithm with vast amounts of data, including fraudulent and nonfraudulent data. The algorithm trains itself on what fraudulent behavior looks like and becomes capable of monitoring transactions and flagging suspicious ones on its own.

Who uses deep learning algorithms?

Deep learning algorithms, and machine learning in general, work widely across a variety of fields and industries. Health care, tech, customer-facing businesses, and entertainment professionals use deep learning algorithms to create more efficient and nuanced products and services. Deep learning algorithms are essential for any business optimizing its operations and processes using data sets.

Read more: 10 Machine Learning Algorithms to Know

Pros and cons of using deep learning algorithms

The pros and cons of deep learning algorithms depend on how the programs interact with data. Some pros of deep learning algorithms include their ability to process unstructured or unlabeled data and create their own insights and responses without human intervention. Deep learning algorithms can interact with and learn from immense volumes of data, which would be impossible for a human to do. These programs find answers or insights that might be difficult for a person to see, and they help support professionals by efficiently handling menial or repetitive processes to free up their time for more meaningful work.

The cons associated with deep learning algorithms include the necessity for high-quality data to obtain the most accurate results, which might be challenging to locate or gather. It’s also important to keep in mind that machine learning depends on the content of the data it trains with, and some inherent biases or discriminatory perspectives might find their way into the data set. And while deep learning algorithms tend toward accuracy, they aren’t infallible; the results always need a human eye to ensure they are correct.

How to get started in deep learning algorithms

If you’re interested in pursuing a career that involves deep learning algorithms, you’ll first want to develop a foundational knowledge of IT skills. Earning a bachelor’s degree in a field like computer science, data science, mathematics, or statistics is helpful. You may also opt to obtain your master’s degree to build stronger skills and a deeper understanding of the technology.  

A data scientist is one example of a role that uses deep learning algorithms. These professionals develop the algorithms and build neural networks that enable deep learning to occur. The average annual salary of a data scientist in the US is $103,500, and the job growth for this career is estimated at 35 percent, which is much faster than average [2].

Next steps with Coursera

Sharpen your machine learning skills and discover more about deep learning algorithms with courses and degrees offered on Coursera. With options like DeepLearning.AI’s Advanced Learning Algorithms, you’ll gain the foundational knowledge required for a career that uses deep learning algorithms. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.

Article sources


ScienceDaily. “Bolstering the safety of self-driving cars with a deep-learning-based object detection system, https://www.sciencedaily.com/releases/2022/12/221212140800.htm.” Accessed March 22, 2024.

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