Deep learning

What is deep learning?

A function of artificial intelligence, deep learning is a type of machine learning that is inspired by the structure of the human brain. Deep learning models process large amounts of unstructured data and can automatically extract features to help categorize images, text and sounds, reducing the need for human input.

Deep learning uses what are referred to as neural networks to complete tasks. At a high level, neural networks consist of various layers of nodes — an input node, one or more hidden layers of nodes and an output node. Each node is connected to another and each connection is assigned an appropriate weight and threshold. If a node produces an output that exceeds the threshold, it sends data to the next layer of nodes for further analysis. Eventually, based on the nodes that are activated, the neural network can make a categorization prediction for the inputted data. The neural network itself is the resulting mesh of connections and pathways between these nodes.

The key differentiator between machine learning and deep learning is in the number of layers of nodes that the input data passes through. The neural networks in machine learning consist of no more than three layers. A neural network with more than three layers would be considered a deep learning algorithm, and deeper neural networks with over 100 different layers can now be built thanks to advancements in computer processing power.

Applications of deep learning can be seen in autonomous vehicle development (distinguishing pedestrians from lampposts or recognizing stop signs), speech recognition, disease detection and identification, content moderation, fraud detection and more.

Benefits of deep learning

Thanks to its ability to handle vast amounts of data without the need for manual, human effort, deep learning is uniquely successful in solving extremely complex problems. Other benefits include:

  • Improved automation: Deep learning algorithms allow brands to offload repetitive and mundane tasks from their team members, saving time and resources.
  • Higher accuracy: By performing a larger number of calculations and making a greater number of connections, the accuracy of deep learning predictions is usually far better than those of simpler models. In some cases, deep learning has proven to outperform humans.
  • Scalable: Rather than plateau at a certain level of performance — a drawback of machine learning — deep learning models continue to improve over time as the size of your data increases.
  • Customer Intelligence: In today’s digital world, brands have access to vast amounts of consumer data. Running this data through deep learning models can help brands make more informed decisions based on consumer purchasing habits, market trends and more.
  • Optimize data annotation: Deep learning models can automatically extract features to help categorize images, text and sounds, reducing the need for human input.

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