What is deep learning?

Deep learning is a powerful type of machine learning that can process unlabeled data and recognize patterns. Deep learning is foundational for many types of AI.

Learning Objectives

After reading this article you will be able to:

  • Define deep learning
  • Differentiate between deep learning and machine learning
  • Understand unsupervised learning, neural networks, and unlabeled data

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What is deep learning?

Deep learning is a type of machine learning that can recognize complex patterns and make associations in a similar way to humans. Its abilities can range from identifying items in a photo or recognizing a voice to driving a car or creating an illustration. Essentially, a deep learning model is a computer program that can exhibit intelligence, thanks to its complex and sophisticated approach to processing data.

Deep learning is one kind of artificial intelligence (AI), and it is core to how many AI services and models function. Large language models (LLMs) such as ChatGPT, Bard, and Bing Chat, and image generators such as Midjourney and DALL-E, rely on deep learning to learn language and context, and to produce realistic responses. Predictive AI models use deep learning to gain conclusions from sprawling collections of historical data.

How does deep learning work?

Usually, using a computer program requires precise inputs for obtaining the correct outputs. Deep learning, in contrast, can take arbitrary or imprecise data and produce a relevant output. For example, a traditional computer program might be able to tell if two digital portraits are exactly the same. A deep learning model might be able to recognize similarities in the portrait's subjects, even if the portraits themselves are different.

Deep learning relies on large data sets and lots of computational power — and as the availability of those two things has increased, deep learning models have become more sophisticated. Today, big data collections and GPU-powered cloud computing services make deep learning more accessible to developers and everyday users than ever before.

What is the difference between machine learning and deep learning?

Machine learning is a type of computer program that can learn without explicit instructions. Deep learning is a specialized kind of machine learning, just as a jet is a specialized kind of airplane. Both involve letting a computer program learn on its own from a set of data. However, deep learning can do more, just as a jet is more powerful than a propeller plane or a glider.

Deep learning can also learn from unlabeled data, while more basic machine learning models may require more context about the data they are fed in order to "learn" correctly. Finally, deep learning models are built using neural networks. Machine learning models may be built on neural networks, but this is not always the case.

How is deep learning used?

Deep learning already has a plethora of applications in the world today, and new uses are still being discovered. Current use cases include:

  • Voice assistants
  • Self-driving cars
  • Predictive models
  • Image creation
  • Natural language processing
  • Conversational AI chatbots
  • Medical research

What is unsupervised learning?

In the field of machine learning, unsupervised learning is a way to identify patterns and associations in a large data set without any context as to what the data set contains. In contrast, supervised learning provides example inputs and outputs to a model. Deep learning can use supervised learning for training models, but its ability to learn unsupervised sets it apart from other types of machine learning.

Imagine a machine learning model is fed examples of news articles, with an indication of what topic each article is about. After sufficient training, this model might be able to "write" an article on a given topic. This is supervised learning.

Now, imagine a deep learning model is fed a series of example news articles, with no guidance as to what each article is about. Such a model, if it is powerful enough, might be able to write an article on a given topic, with the topic alone provided as the input. This is unsupervised learning.

What is unlabeled data?

Unlabeled data is data without classifications, tags, or labels. Unlabeled datasets can contain any arbitrary data and can take any form: random photos, video compilations, long lists of file names, log data, or a combination of all of the above. The news articles provided without context (from the previous example) would be an example of unlabeled data.

Deep learning models are able to contextualize and "understand" unlabeled data. And typically, the more data they are fed, the more sophisticated the models become.

Unlabeled data and object storage

Unlabeled data is often unstructured as well. Unstructured data does not follow any particular format, and thus can contain any type of digital information. Object storage is often used for saving unstructured data of this kind. Such data collections can grow indefinitely, and object storage is a highly scalable, fairly cost-effective way to store them.

Deep learning models grow more effective when they are given large data collections to learn from, even when that data is unlabeled and unstructured. Object storage is therefore an important resource for deep learning models.

What is a neural network?

A neural network is a type of machine learning architecture based on how a human brain functions. Neural networks are a collection of nodes; each node is its own processing unit. Data that is statistically significant gets passed along from one node to the next.

These nodes are spread across at least three layers: an input layer, a hidden layer, and an output layer. Usually there are several nodes in each layer. There can be multiple hidden layers, and deep learning models tend to have many.

Think of a neural network as a team working together to solve a problem. Each member of the team is responsible for one aspect of the problem, and once their role is fulfilled, they hand it off to the next team member. Finally, the team arrives at a full solution together.

Neural networks have existed for decades, but modern-day deep learning uses more layers than neural networks of the past. The deep learning models of today also have access to vastly more compute power and data than ever before, enabling developers to accelerate the advancement of AI technology.

How does Cloudflare enable the construction of deep learning models?

Cloudflare helps enable developers to easily build AI applications that can be accessed from anywhere with minimal latency. Cloudflare Workers AI provides access to serverless GPUs on Cloudflare's global network for running advanced machine learning models. And Cloudflare R2 is object storage with no egress fees for more cost-effective storage of large data sets, which can be used to train deep learning models.

Learn all about Cloudflare for AI.