Deep learning vs. machine learning – what are the differences?

Many people are suspicious of artificial intelligence. They don’t understand how computers can ‘learn’ and make intelligent decisions. Yet, the concept of AI can be understood by anyone.

Machine learning and deep learning are the two most important concepts in making AI possible. The two terms are often conflated, but they describe two fundamentally different methods with their own areas of application.

Both machine learning and deep learning are subsections of artificial intelligence. Both approaches result in computers being able to make intelligent decisions. Deep learning, however, is a subtype of machine learning, as it’s based on unsupervised learning.

In both cases, this intelligence is limited to individual areas of application. We speak of so-called “weak artificial intelligence,” as opposed to “strong artificial intelligence,” which would have a human-like capacity to make intelligent decisions across many areas and situations.

Both technologies rely on large quantities of data being available for systems to learn from. That’s where the similarities end, though.

Historically speaking, machine learning is the older and simpler technology. It works with an algorithm that adapts when it receives human feedback. One requirement for making use of this technology is the availability of structured data. First, the system is fed structured and categorized data, and in this way, it understands how to classify new data of the same type. Depending on the classification, the system then carries out programmed activities. For example, it can distinguish whether a photo features a dog or a cat, and allots the files to their respective folders.

An initial application phase is followed by the optimization of the algorithm using human feedback – for this, the system is informed about any incorrect classifications and the correct categorizations.

With deep learning, structured data isn’t necessary. The system works with multi-layer neural networks that combine different algorithms that are modeled on the human brain. That’s why the system can also process unstructured data.

The approach is most suitable for complex tasks where not all aspects of objects can be categorized beforehand.

Important: In deep learning, the system finds suitable differentiation characteristics in the files by itself, with no need for any external categorization. In other words: training by the developer isn’t necessary. The system itself considers whether to change classifications or produce new categories based on new input.

While machine learning can already work with a manageable data pool, deep learning requires much more data. For the system to produce reliable results, more than 100 million data points should be available.

The technology for deep learning is also more costly to implement. It takes more IT resources and is significantly more expensive than machine learning, meaning that – for now, at least – it isn't an option for mainstream businesses.

Machine learning could be seen as a precursor to deep learning. In fact, all tasks that can be carried out by machine learning can also be processed by deep learning. It shouldn’t even be necessary to weigh up deep learning vs. machine learning.

Since deep learning requires significantly more resources, though, it isn’t an efficient procedure. The areas of application for both technologies are therefore clearly separated, and if machine learning can be used then machine learning will be used.

Using both technologies provides an enormous competitive advantage to companies, as both machine learning and deep learning are far from standard in the day-to-day business environment.

Online marketing: What marketing measures create results? Humans are generally not very good at surveying large quantities of data and delivering reliable estimations. This is where marketing analytics tools, based on machine learning, come in. These can evaluate existing data and make reliable forecasts as to the kind of content that would lead to conversions; what content customers want to read; and which marketing channels primarily result in a purchase.

Customer support:  Chatbots can be based on machine learning. They are oriented towards keywords included in the user’s query, and can guide customers to the information they are looking for through queries and yes/no questions in the dialog.

Sales: If it works for Netflix and Amazon, it can also be used in sales. Thanks to machine learning, systems can successfully predict which products and services existing customers might also be interested in. Here, the systems are able to provide very detailed recommendations which, in the case of large product ranges and highly customizable products, simplify sales.

Business intelligence: Machine learning can also be used to visualize important business data and to make forecasts easier to understand for the human decision-maker.

IT security: Unlike with machine learning, IT and cybersecurity systems that are based on deep learning not only recognize pre-defined dangers, but also new, hitherto unknown threats, as these are picked up as anomalies by the neural network’s pattern recognition. The effectiveness of security measures can be dramatically increased with the help of deep learning.

Customer support: Chatbots that are based on deep learning understand human language, and don’t rely on certain keywords being used. The dialog is much more efficient and the solution offered is more accurate.

Content creation: With deep learning, content creation can be automated. If enough content is available as a data pool, the system can create new content from it and perform translations autonomously.

Speech assistants: Digital assistants like Siri, Alexa, and Google are based on deep learning. In business contexts, too, the first speech assistants are now being used. For example, users can ask them in a natural way to place orders, send emails, create reports, or carry out research.

Beyond the areas of application listed here, both technologies can also be used in many more areas, such as in medicine, science, or mobility.