Glossary term

Deep Learning

What Is Deep Learning?

Deep learning teaches computers to do something that comes naturally to humans—learn by example. Deep learning is a subset of machine learning, differentiated by the way it structures algorithms in layers of interconnected, decision-making nodes to model more complex data. These layers create an artificial neural network that can make intelligent decisions on its own, such as analyzing images and natural language text, enabling autonomous vehicles to detect stop signs, and identifying patterns that indicate cyberattacks before they occur.

Deep learning methods are often referred to as black box models—models that are nearly impossible to explain and comprehend, even by experts who can see their structures or weights.

Though deep learning isn’t going to replace human intelligence, some models have achieved absolute accuracy and exceeded human ability in certain kinds of simulations. Deep learning opens the door to a world like the ones we’ve seen in science fiction movies (minus the murderous robots), where AI can solve a wide variety of cognitive and reflexive problems.

Why Is Deep Learning So Important?

Deep learning goes one step further than existing machine learning algorithms and surpasses the already impressive capabilities. Deep learning models, unlike basic machine learning models, can make accurate decisions without human direction, making them more scalable and more powerful.

With machine learning models, data scientists feed data into a computer to gauge responses, such as whether a customer churned or not based on certain behaviors. Data scientists confirm accurate predictions and correct the wrong ones. Over time, the algorithm is “taught” to improve performance.

With deep learning, the computer is given a mix of correctly labeled and unlabeled data to search for patterns on its own. The labeled data provides guidance, but the programmers do not need to oversee the model. Over time, deep learning algorithms will find patterns or associations that may not be apparent to human data scientists and help make outcomes even more accurate.

Have you noticed how eerily accurate your Amazon recommendations for what to buy next are? That’s all due to deep learning-based recommender engines, which are miles ahead of traditional nearest neighbor methods used in years past.

Common types of deep learning that demonstrate this excelled accuracy and scalability include:

Key Examples of Deep Learning

Real-world deep learning applications are already integrated into our daily lives—we as users are barely aware of the deeply complex data processing that goes on behind the scenes. Some of these examples include:

Customer service bots

Nowadays, many chatbots use a more straightforward form of AI to deal with online customers. However, with deep learning, chatbots will be able to generate multiple solutions and respond to more ambiguous questions, ultimately streamlining and improving customer service.

Law enforcement

Law enforcement uses deep learning to detect dangerous patterns that could indicate criminal activity. In addition, there are CNN and RNN networks optimized for speech recognition and image classification to improve the efficiency of the investigation process and predictive policing.


Recently, healthcare researchers have been able to use deep learning to detect cancer cells using advanced microscopes that can analyze high-dimension data. While humans can’t process this type of data, the DL algorithms can identify cancer cells using this method with a high level of precision. CNNs have also been shown to be more accurate at reading chest x-rays to detect pneumonia than humans.


Deep learning models have been programmed into satellites and monitoring systems globally to help identify dangerous objects within locations of interest, such as war zones. These systems can help identify unsafe zones for troops to enter before humans go to investigate.

Researchers also hope to equip soldiers with deep learning devices on the battlefield where traditional AI and machine learning solutions would struggle with limited training data and low capacity to rapidly identify patterns.

Automated Driving

Tesla, Waymo, and Nvidia (among others) are using CNNs that continuously gather data from cameras, RADAR, and LiDAR devices. This constant stream of data automatically detects things like pedestrians in roadways, providing potentially life-saving information in busy environments to enable autonomous vehicles to make better, faster decisions.

Critical Challenges of Deep Learning

While older learning algorithms initially increase in performance, then plateau after a certain amount of data is added, deep learning algorithms continue to optimize performance regardless of how much data is added to them. Though this means their optimal performance is much higher than previous learning algorithms, it also means that it takes vast amounts of data to out-perform old models. In other words, they’re one of the most data-hungry models we have.

Another challenge of deep learning models is their inability to effectively deal with data that is different from their training set’s distribution. When deep learning models are fed data that has some variations from their training data, they often perform poorly.

The Future of Deep Learning

Deep learning has many exciting capabilities—both now and in the future. Going forward, we can expect to see deep learning affect the way we work, shop, and live, powering innovations from image-based searches to more exact fraud detection. It all starts with data, and how your organization decides to use it.

Want to learn more about real-world applications of deep learning? Check out our blog post on autoencoders, an artificial neural network used to help reduce the noise in data.

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