Deep Learning vs. Machine Learning: What Enterprises Need to Know

What's the difference between deep learning and machine learning?

The terms deep learning and machine learning are sometimes used interchangeably, but that's a mistake. They're closely connected, but not synonymous: Deep learning shares machine learning's core capability of improving its performance without human intervention, but it's much more structurally complex.

To avoid confusion before going further, let's precisely define the two terms.

What is machine learning?

Machine learning is an offshoot of artificial intelligence that uses algorithms to examine large data sets and learn from them, without any human intervention aside from the initial programming and training process. Machine learning algorithms give applications and operating systems the ability to steadily improve their performance without necessarily needing to be reprogrammed.

ML models can be developed through supervised learning, unsupervised learning, or reinforcement learning techniques. These differ based on their training data. The supervised machine learning method uses labeled data sets, and its opposite uses unlabeled data. Both are capable of complex pattern recognition, but unsupervised models can't predict outcomes. Reinforcement learning follows a trial-and-error approach to gradually increase an ML model's accuracy and problem-solving efficiency with feedback and rewards.

What is deep learning?

Deep learning is an advanced subset of ML. It's distinguished by its interconnected and at least triple-layered series of nodes known as an artificial neural network (ANN). The various layers in a deep learning model allow it to process massive amounts of structured, unstructured, or semi-structured data and analyze it with a degree of complexity modeled after the human brain.

For example, if image data is ingested by a deep neural network, each layer carefully parses through the data and identifies specific features of the images. This process, known as forward propagation, ultimately uses those identified characteristics to define each image's content. A machine learning model could perform this task, but it would have to be "trained" on labeled structured data for image recognition to be possible. Deep learning algorithms can serve the same purpose while only looking at raw, unstructured image data.

Deep learning systems require many resources to successfully run: state-of-the-art hardware, robust enterprise data architecture, scalable cloud storage, and high-level computing power. As a result, the technology hasn't yet been adopted as much as traditional machine learning. But as enterprises come to need advanced versions of ML-based tools for functions like image recognition and natural language processing (NLP), they'll begin turning to deep learning.

Comparing deep learning and machine learning

The biggest difference between deep learning and machine learning is complexity. For a neural network to be called "deep," it must contain at least three layers—one for input, another for output, and one or more hidden layers that allow for hierarchical processing. Neural networks that have only two layers, for input and output, are considered machine learning rather than deep learning.

This level of complexity also relates to the ability of either technology to improve its performance over time. ML models can certainly do this as more data is introduced to them. But if any errors occurred during the training phase or when their underlying algorithms were written, human engineers must step in to do some reprogramming. By contrast, deep learning models examine their own errors through back propagation. They can—to a certain extent—account for biases or other flaws without explicit programming, and compensate for those issues in future calculations.

Is deep learning 'better' than machine learning?

Because deep learning is capable of executing more complex tasks than machine learning and has the capacity for advanced self-correction, someone might at first glance think it's a superior technology. But deep learning isn't better than machine learning—at least not by default.

The layers and nodes in a deep learning system are modeled after human intelligence and can process the most complex unstructured data. But it takes time, effort, and skill for a data engineer to train a deep learning model—days or even weeks of intensive work. Attempting to accelerate the process by increasing the model's learning rate can lead to a weaker network.

By contrast, an experienced machine learning engineer doesn't need as much time to put an ML model through supervised, unsupervised, or reinforcement learning. ML tools are superb for processing structured data at the scale enterprises require—while also handling some unstructured data—and are less difficult and resource-intensive to implement and maintain.

In summation, the best way to look at these two technologies is to understand that they both offer significant business value. It's all a matter of finding the right uses for each method and using leading-edge resources to support them, including a hybrid cloud-based data management strategy anchored by an agile data analytics platform.

Deep learning vs. machine learning: Use cases

Although the complexity, extreme compute power, lengthy training, and IT costs associated with deep learning keep it from being commonplace today, a number of notable use cases for the technology have emerged.

Noteworthy uses of deep learning

Law enforcement: Sensors and cameras augmented by computer vision algorithms and deep neural networks help police or federal agents respond faster to serious crimes. Other deep learning-based technologies relevant to law enforcement include speech recognition tools that use high-level NLP and neural networks trained to recognize the signs of financial crimes.

An April 2021 study detailed researchers working to take computer vision even further—as a crime prediction method. Though it will be some time before such tools are viable for widespread use, the study claimed that deep neural networks predicted overall crime numbers in various cities at rates as high as 75%.

Vehicles: If cars that can safely drive without human operation become a regular presence on the road, deep learning will be one of the biggest reasons why. Computer vision powered by convolutional neural networks (CNNs) is what allows prototypical driverless cars to navigate around pedestrians, vehicles, and obstacles, and deep learning algorithms help these autos find the most efficient routes.

Biology: Understanding protein structures is immensely valuable to many areas of science. For example, when a protein misfolds and its structure mutates, it can lead to the development of incurable neurodegenerative diseases such as Parkinson's and fatal familial insomnia.

Yet billions of protein structures remain unknown—which is why the emergence of a deep learning model that can reliably predict them is a massive breakthrough. Innovations like this will assist in drug development, disease research, genetics, and much more.

Document analysis: This process, also known as text analytics, involves examining massive stores of text-heavy documents—medical records, invoices, tax returns, insurance claims, and so on. It's critical to enterprises across all major industries, and deep learning makes it much faster and more accurate.

Fraud detection—in insurance, banking, and accounting—is perhaps the best-known use of text analytics. But the process also allows organizations to verify compliance with complicated government regulations, identify patterns of malfeasance like insider trading, and run qualitative assessments of various business processes.

Major machine learning applications

Sentiment analysis: The process of sentiment analysis is one of the key reasons why machine learning is so useful in marketing. An ML algorithm can parse hundreds of thousands of social media posts, emails, reviews, and data from all other customer feedback channels to accurately determine how consumers en masse feel about a product or service.

ML-driven sentiment analysis also works on a much smaller but equally important scale—e.g., creating profiles for individual consumers to more effectively recommend products they'll like.

Information security: A single data breach can cost an enterprise several million dollars, and some of them cost far more. ML has significant potential to strengthen enterprises' information security postures.

For example, a machine learning algorithm can be trained to identify exactly what level of network access each employee needs based on their positions and assign that access—no more, no less. Additionally, ML's superb pattern recognition capabilities are ideal for the early identification of potential cyberthreats, allowing security teams to take proactive countermeasures.

Language translation: If someone types a phrase from one language into a translation tool and instantly sees the phrase's equivalent in another language, it's almost certainly the result of machine learning. Because ML models for translation steadily boost their accuracy over time as they ingest new data, they reliably translate even more obscure idioms and regional variations of various languages.

Social media: It's almost impossible to imagine Facebook, Twitter, Instagram, TikTok, and other major social channels operating as they do now without ML. The sites are powered by ML algorithms that analyze active users' actions to determine how content displays on individuals' feeds and assign ads based on relevance.

The future of machine learning and deep learning

Both machine learning and deep learning have yet to reach their full potential. Further adoption will depend on additional development of the technology, extending into the next several years, decades, and beyond.

Nevertheless, machine learning and deep learning have current real-world applications for many enterprises. Organizations should primarily focus on determining which of these technologies best suits their specific business needs. Companies must also consider the ethical issues surrounding these subsets of AI, including biased models, customer data privacy, and safety.

The data generated by both deep learning and ML must be properly managed and analyzed for it to have maximum value, and Vantage, Teradata's connected data analytics platform, is more than equal to the task.

To learn more about the potential of deep learning and ML, check out case studies from Teradata customers Danske Bank and O2 Czech Republic.

AI & Machine Learning: Lessons and Opportunities