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 deep learning engineer studies deep learning models and determines how to improve their accuracy.

Key takeaways

Deep learning is a subset of machine learning involving neural networks that mimic the learning process of the human brain.

  • Deep learning includes a multilayered neural network architecture, where data enters through an input layer, is processed by hidden layers, and provides a result in the output layer.

  • Deep learning has wide-ranging applications, from self-driving cars and chatbots to facial and speech recognition.

  • You can build proficiency in deep learning by building skills in TensorFlow, machine learning and AI programming languages, calculus, natural language processing, and neural network architecture.

Learn more about deep learning systems and how to work with them, as well as how to start a career in deep learning. If you’re ready to start building expertise in deep learning, enroll in the Deep Learning Specialization from DeepLearning.AI. You’ll have the opportunity to gain experience with building and training deep neural networks, convolutional neural networks, recurrent neural networks, and more in as little as three months. Upon completion, you’ll have earned a career certificate for your resume.

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 our skills. 

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.

Read more: What Are Deep Learning Models? Types, Uses, and More

What are AI, ML, and 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:

What is deep learning used for? 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.

Chatbots

Deep learning chatbots designed to mimic human intelligence (like ChatGPT) 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 between 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 an online course, such as Introduction to Generative AI, offered by Google. Taking a 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 with lower costs.

There are a wide variety of career opportunities that utilize deep learning knowledge and skills. In addition to data, machine learning, 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 PhDs, but it is possible to enter the field with a bachelor's degree and relevant experience. Proficiency in coding and problem-solving are base skills necessary to explore deep learning.

Career Professional 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 Professional Certificate program to improve your resume and focus your studies on job-ready skills. Here are a couple of career-focused Professional 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.

Guides and resources to keep you on track

Curious about what comes next? Learn about the latest career trends and get expert insights into your industry by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep exploring careers, skills, and courses related to deep learning, machine learning, and AI, check out these additional free resources:

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