What Is Deep Learning? Definition, Examples, and Careers

Written by Coursera • Updated on

Deep learning falls under the umbrella of machine learning and AI, eliminating some of machine learning's data preprocessing with algorithms. Learn more with this overview of deep learning.

[Featured image] Artificial intelligence pathways

Deep learning is related to machine learning based on algorithms inspired by the brain's neural networks. Though it sounds almost like science fiction, it is an integral part of the rise in artificial intelligence (AI). Machine learning uses data reprocessing driven by algorithms, but deep learning strives to mimic the human brain by clustering data to produce startlingly accurate predictions.

Examples of deep learning

Deep learning is generating a lot of conversation about the future of machine learning. Technology is rapidly evolving, generating both fear and excitement. While most people understand machine learning and AI, deep learning is the "new kid on the block" in tech circles and generates both anxiety and excitement.

Deep learning is also known as neural organized learning and happens when artificial neural networks learn from large volumes of data. Deep learning algorithms perform tasks repeatedly, tweaking them each time to improve the outcome. The algorithms depend on vast amounts of data to drive "learning."

Current estimates predict that 1.145 trillion MB of data is produced every day, and it is this staggering amount of data production that makes deep learning possible [1]. The vast increase in data creation is the driving force behind the rise in deep learning capabilities. Though deep learning can sound mysterious, the truth is that most of us are already using deep learning processes in our everyday lives.

Read more: Deep Learning vs. Machine Learning

Below are a few of the tasks supported by deep learning:

Virtual assistants

Do you use Alexa, Cortana, or Siri? Popular virtual assistants use deep learning to understand the language and terminology humans use when interacting with them. As a result, they become more adept at providing the information requested.

Driverless vehicles

Autonomous vehicles are already on our roadways. Deep learning algorithms help determine whether the object on the road is a paper sack, another vehicle, or a child and react accordingly.


Chatbots have gained popularity and appear on many websites used every day. Chatbots powered by deep learning can increasingly respond intelligently to an ever-increasing number of questions. The deeper the data pool from which deep learning occurs, the more rapidly deep learning can produce the desired results.

Facial recognition

Facial recognition plays an essential role in everything from tagging people on social media to crucial security measures. Deep learning allows algorithms to function accurately despite cosmetic changes such as hairstyles, beards, or poor lighting.

Medical science

The human genome consists of approximately three billion DNA base pairs of chromosomes. Machine learning is helping scientists, and medical professionals create personalized medicines, diagnose tumors, and is undergoing research and utilization for other pharmaceutical and medical purposes.

Careers in deep learning

As in all manner of machine learning and artificial intelligence, careers in deep learning are growing exponentially. Deep learning offers organizations and enterprises systems to create rapid developments in complex explanatory issues.

Data Engineers specialize in deep learning and develop the computational strategies required by researchers to expand the boundaries of deep learning. Data Engineers often work in specific specialties with a blend of aptitudes across various research ventures.

Typical jobs in deep learning

There is a wide variety of career opportunities that utilize deep learning knowledge and skills. In addition to Data, Machine, and Deep Learning Engineers, these include:

Software engineers

Data analysts

Data scientists

Software engineers


Software developers

Research scientists

Full-stack web developers

Natural language process engineers

A career in deep learning offers a multitude of pathways to combine natural aptitudes with experience and education.


Salary outlook

A Machine Learning Engineer has an average base salary of $116,743 in the US, according to Indeed [2]. While deep learning is considered a subset of machine learning, it is more sophisticated. Salaries for engineers specializing in deep learning reflect the value of that specialized knowledge.

The deep learning skills you need

Data science is the foundational building block for career aspirations in deep learning. Many subjects are intricately intertwined in developing the needed skills for deep learning. Zeal and patience, combined with the proper training and education, can open doors to an exciting career in innovative technology. Some of the most fundamental skills needed include:

  • TensorFlow

  • Artificial intelligence

  • Apache Kafka

  • Other programming languages for machine learning

  • Physics

  • Calculus

  • Dynamic programming

  • Applied mathematics

  • Natural language processing

  • Neural network architecture

Becoming proficient in deep learning involves both technical and non-technical expertise. Since its inception, artificial intelligence and machine learning have seen explosive growth. The advent of deep learning has sped up the evolution of artificial intelligence. Those exploring a career in deep learning will find themselves poised to explore the latest frontier in machine learning.

Necessary education

Deep learning is a subset of machine learning, so understanding the basics of machine learning is a good foundation on which to build. Though many Deep Learning Engineers have PhDs, it is possible to enter the field with a bachelor's degree and relevant experience. Proficiency in coding and problem solving are the base skills necessary to explore deep learning.

Read more: How Long Does It Take to Get a PhD?

From an educational perspective, there are several notable programs in the deep learning space. Coursera makes exploring options for both degree plans and additional certifications easy.

What kind of experience is required?

Experience can include time in the workforce, and time invested in courses, certifications, and autodidactism can help prepare you for a place in the realm of deep learning. Three primary factors are making deep learning readily accessible. Powerful computing hardware is less expensive, cloud computing offers access to a wealth of data,  and there are numerous open-source deep learning platforms like Caffe, Theano, and TensorFlow.

Read more: Machine Learning Interview Questions and Tips for Answering Them

If you have experience on the development side of computer science, you may be well-positioned to enter the field of deep learning. Experience in the intricacies of common languages such as Python is essential for a career in deep learning.

Mastering as many languages as possible will help build the flexibility and knowledge needed to excel in the field.

Honing software engineering skills such as data structures, Github, sorting, searching, optimizing algorithms, and a deep understanding of the software development life cycle is crucial to developing the sophisticated skills needed for deep learning.

Interested? Take the next steps

Forge ahead with your machine learning plans with a self-paced course from an industry leader, like Machine Learning Rock Star from SAS or Machine Learning from Stanford. After you complete each course or Specialization, you’ll have a certificate to add to your resume or LinkedIn profile.

Related articles

Articles sources

1. Techjury. “How Much Data Is Created Every Day in 2022?, https://techjury.net/blog/how-much-data-is-created-every-day/#gref." Accessed March 31, 2022.

2. Indeed. “Machine Learning Engineer Salaries, https://www.indeed.com/career/machine-learning-engineer/salaries." Accessed March 31, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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