What Is Deep Learning?

Using more power and more extensive data models, Deep Learning can be more capable than a typical Machine Learning system

Deep Learning is a subset of Machine Learning that uses neural networks, significant amounts of computing power, and huge datasets to create systems that can learn independently. It can perform more complex actions than traditional Machine Learning models.

Understanding Deep Learning

To understand Deep Learning and how it differs from Machine Learning, you need to understand neural networks.

Neural Network Layers

Neural networks—or, more accurately, Artificial Neural Networks—are computing systems composed of multiple layers or nodes. Layers are small systems dedicated to specific types of tasks. When those layers are combined, the resulting system can learn how to tackle complex, multi-dimensional tasks in a way that simulates the human brain.

Deep Learning systems have at least three layers, but usually more (many have 100+ layers). In this case, "deep" refers to the multiple layers of these systems and contrasts with less-complex Machine Learning tools.

At a minimum, Deep Learning systems have three layers: input, processing, and output. Data is fed into the system at the input layer, the processing layer performs the system's intended functions, and the output layer delivers the results or actions. The more layers the Deep Learning tech has, the more powerful the system is and the more capable of performing complex tasks.

System Needs

Running a system with so many layers requires significant computing power. It also requires enormous data sets to train the Deep Learning system on its tasks. Like some Machine Learning models, Deep Learning is trained using labeled, structured data.

After initial training, Deep Learning systems tend to require less human intervention than ML models. The longer they run, the better Deep Learning systems are at analyzing data, detecting patterns, making predictions, and taking action.

How Deep Learning Works

Deep Learning is an Artificial Intelligence application in which multiple layers of neural networks are combined into a single, powerful system. Suppose Artificial Intelligence is the broadest category for this kind of computing. In that case, Machine Learning is a sub-area of Artificial Intelligence, while Deep Learning is a sub-area of Machine Learning. As a result, these systems often help power AI.

Deep Learning tech can perform more functions than Machine Learning. It also requires significantly more computer processing power since its functions are still being developed and refined. Still, the materials needed to deliver it—extremely powerful computers at relatively affordable prices and huge datasets—have only become available in recent years, leading to growth in its use.

Deep Learning vs. Machine Learning

The two key differences between Deep Learning and Machine Learning are how they define features and take action.

ML models can be trained to detect patterns or recognize objects. Still, to do that, a human programmer needs to define the features of those objects (if the model is meant to detect a stop sign, the programmer needs to define the characteristics of a stop sign for the model). Deep Learning systems, on the other hand, can figure out the characteristics of a stop sign without any human input and then apply those characteristics to analysis tasks.

The ability to take action is another differentiator between Deep Learning and Machine Learning. ML models are often best used for analyzing data, making predictions, and executing tasks of limited complexity or risk (such as filtering spam or recommending content).

While Deep Learning can do all those things, it can also be tasked with performing complex actions without human supervision and at an extremely fast pace. Automated stock trading systems used in the financial industry are a good example.

An ML model could analyze historical stock performance and make recommendations to a stockbroker. A Deep Learning model could analyze performance and, instead of making a recommendation, would automatically buy and sell stocks, based on its logic, without involving a human and at speeds many times faster than a person.

Common Uses

Because Deep Learning is composed of multiple specialized and powerful layers, it can perform significantly complex tasks. It is used in industries like:

  • Financial Services: Used for automated, algorithmic stock trading, for near-instantaneous decisions on credit applications, and for detecting and responding to possible fraud.
  • Healthcare: Is used for some medical research and reviewing medical imaging of test results to deliver diagnoses more accurately and quickly than humans can.
  • Voice Interfaces: This tech excels in handling ambiguity or situations lacking clarity, making it valuable for voice-interface systems. Because spoken language can be indirect, meaning and intent uncertain (think about how the same word can mean different things given the context), Deep Learning's ability to learn progressively helps it deliver valuable results for spoken interfaces like Siri, Google Now, or language translation tools.
  • Autonomous Vehicles: Self-driving vehicles present some of the most complex challenges in computing. These systems help address these challenges because of their ability to perform numerous complex calculations almost instantaneously. For example, a driverless car needs the ability to calculate the distance to other cars and pedestrians, to predict how other vehicles and objects may behave (will that person step off the curb? Is that ball bouncing into the road?), and adjust on the fly.
  • What is transfer learning in deep learning?

    Transfer learning aims to test how well a deep learning system can solve problems similar to the ones it's already studied. For example, researchers might take a program that was trained to identify forks and see how well it works on spoons.

  • What is epoch in deep learning?

    An epoch is a process that uses all of an algorithm's training data at once. Simply put, it's a single "cycle" in which all of the information passes through the system.

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