What Is Deep Learning? Definition, Examples, and Careers

Written by Coursera Staff • Updated on

Deep learning is a method that trains computers to process information in a way that mimics human neural processes. Learn more about deep learning examples and applications in this article.

[Featured image] A digital rendering of white lines and their connection points on a dark background representing artificial intelligence pathways

The field of artificial intelligence (AI) and machine learning (ML) is rapidly evolving, generating both fear and excitement. While many people have a general understanding of ML and AI, deep learning is a special type of machine learning that can be more challenging to describe.

You can learn more about deep learning systems and how to work with them in the following article, or start your journey with the popular course, Deep Learning Specialization from DeepLearning.AI.

What is deep learning?

Deep learning is a branch of machine learning that is made up of a neural network with three or more layers:

  • Input layer: Data enters through the input layer.

  • Hidden layers: Hidden layers process and transport data to other layers.

  • Output layer: The final result or prediction is made in the output layer.

Neural networks attempt to model human learning by digesting and analyzing massive amounts of information, also known as training data. They perform a given task with that data repeatedly, improving in accuracy each time. It's similar to the way we study and practice to improve skills. 

Read more: Deep Learning vs. Machine Learning

Deep learning models

Deep learning models are files that data scientists train to perform tasks with minimal human intervention. Deep learning models include predefined sets of steps (algorithms) that tell the file how to treat certain data. This training method enables deep learning models to recognize more complicated patterns in text, images, or sounds.

AI vs. machine learning vs. deep learning 

AI, machine learning, and deep learning are sometimes used interchangeably, but they are each distinct terms.

Artificial Intelligence (AI) is an umbrella term for computer software that mimics human cognition in order to perform complex tasks and learn from them.  

Machine learning (ML) is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks. 

Deep learning is a subset of machine learning that uses several layers within neural networks to do some of the most complex ML tasks without any human intervention. 


Watch this video from DeepLearning.AI's course, Neural Networks and Deep Learning, to learn more about deep learning and neural networks:

Examples of deep learning

Deep learning is a subset of machine learning that is made up of a neural network with three or more layers. A neural network attempts to model the human brain's behavior by learning from large data sets. Deep learning drives many AI applications that improve the way systems and tools deliver services, such as voice-enabled technology and credit card fraud detection.

Self-driving cars

Autonomous vehicles are already on our roadways. Deep learning algorithms help determine whether there are other cars, debris, or humans around and react accordingly.


Deep learning chatbots designed to mimic human intelligence (like Chat-GPT) have gained recent popularity due to their ability to respond to natural-language questions quickly and often accurately. 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 other medical professionals to create personalized medicines, and diagnose tumors, and is undergoing research and utilization for other pharmaceutical and medical purposes.

Speech recognition

Similar to facial recognition, deep learning uses millions of audio clips to learn and recognize speech. It can then power algorithms to understand what someone said and differentiate different tones, as well as detect a specific person's voice.

How to get involved with deep learning technology

Whether your interest in deep learning is personal or professional, you can gain more expertise through online resources. If you're new to the field, consider taking a free online course like Introduction to Generative AI, offered by Google. Taking a free class from an industry leader in technology can help you build the foundational knowledge you need to start an independent project or decide whether or not you want to pursue a career in deep learning. Once you feel you have the basics down, you can begin experimenting with open-source deep learning platforms such as Caffe, Theano, and TensorFlow.

Core deep learning skills and technologies

Becoming proficient in deep learning involves extensive technical expertise. The list below outlines some specific skills and systems you'll need to learn if you want to get into deep learning professionally.

  • TensorFlow, Apache Kafka

  • Machine learning and AI programming languages

  • Physics

  • Calculus

  • Dynamic programming and coding

  • Applied mathematics

  • Natural language processing

  • Neural network architecture

Careers in deep learning

Just like in machine learning and artificial intelligence, jobs in deep learning are experiencing rapid growth. Deep learning helps organizations and enterprises develop ways to automate tasks and do things better, faster, and cheaper.

There are a wide variety of career opportunities that utilize deep learning knowledge and skills. In addition to data, machine, and deep learning engineers, these include:


Education requirements

Deep learning is a subset of machine learning, so understanding the basics of machine learning is a good foundation to build on. Many deep learning engineers have Ph.D.s, but 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?

Career Certificates

If you already have some of the skills mentioned above or you want to switch to a career in deep learning from a related field, you might consider a certificate program to improve your resume and focus your studies on job-ready skills. Here are a couple of career-focused certificate programs to get you started:

Gaining deep learning experience

After you've mastered some of the skills like those listed above, you might be ready to apply for jobs in data science and machine learning. Even an entry-level job as a developer or data analyst can provide exposure to machine learning algorithms and models, as well as those that involve deep learning.

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.

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 a career in deep learning.

Learn about deep learning with expert-level guidance on Coursera

Led by AI expert Andrew Ng, this 100-percent self-paced, online Deep Learning Specialization includes the following courses for a strong overview of deep learning techniques and fundamentals:

  • Neural Networks and Deep Learning

  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  • Structuring Machine Learning Projects

  • Convolutional Neural Networks

  • Sequence Models

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