Deep learning vs. machine learning

Deep learning is a spe­cial­ized subset of machine learning that employs multi-layered neural networks. In contrast, machine learning often relies on simpler al­go­rithms like linear models or decision trees. Deep learning’s deeper network structure makes it possible for it to detect more complex patterns in larger datasets.

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Machine learning and deep learning are subfields of ar­ti­fi­cial in­tel­li­gence. Deep learning, a subset of machine learning is based on un­su­per­vised learning.

Both machine learning and deep learning make it possible for computers to make in­tel­li­gent decisions, however, the in­tel­li­gence is limited to in­di­vid­ual areas. Such types of ar­ti­fi­cial in­tel­li­gence are referred to as “weak AI”. Strong AI, on the other hand, reflects a human-like capacity to make in­tel­li­gent decisions across a wide range of scenarios and contexts.

What are the dif­fer­ences? Deep learning vs. machine learning

Of the two, machine learning is the older and simpler tech­nol­o­gy. It uses adaptable al­go­rithms that modify them­selves based on human feedback. For it to work, it needs struc­tured data. Having struc­tured data that is cat­e­go­rized helps the system to learn how to classify similar data. Depending on the clas­si­fi­ca­tion, the system carries out tasks specified by the program.

For example, a machine learning system can determine whether a photo contains a cat or a dog and then move the files to the re­spec­tive folders ac­cord­ing­ly. After the first round, human feedback is given to optimize the algorithm. The system is made aware of mis­clas­si­fi­ca­tions as well as how to correctly cat­e­go­rize the data that was mis­clas­si­fied.

With deep learning, struc­tured data isn’t necessary. This is because the system works with multi-layer neural networks that are modeled on the human brain and combine different al­go­rithms. This approach is most suitable for complex tasks where not all aspects of the data 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, it doesn’t need to be trained by de­vel­op­ers. The system itself considers whether to change clas­si­fi­ca­tions or produce new cat­e­gories based on new input.

While machine learning can work with smaller datasets, deep learning requires far more data. For a deep learning system to produce reliable results, it should have more than 100 million data points to work with. Deep learning also requires more IT resources and is sig­nif­i­cant­ly more expensive than machine learning.

Overview of dif­fer­ences between machine learning and deep learning

How do the use cases for deep learning and machine learning differ?

Machine learning can be seen as a precursor to deep learning. In fact, deep learning is capable of doing all the tasks that machine learning is able to do. That’s why it’s not necessary to compare deep learning and machine learning in terms of their ca­pa­bil­i­ties.

Deep learning requires sig­nif­i­cant­ly more resources though, making it the less efficient option for use cases where both machine learning and deep learning can be applied. Simply put: If machine learning can be used, it should be used.

Since both machine learning and deep learning are still es­tab­lish­ing them­selves in standard business settings, using both tech­nolo­gies can provide companies with an enormous com­pet­i­tive advantage.

Deep learning vs. machine learning — Use case com­par­i­son

In online marketing, busi­ness­es often use marketing analytics tools that employ machine learning. These can evaluate existing data and make reliable forecasts as to the content customers want to read, the type of content that will likely lead to con­ver­sions and the marketing channels that most often result in purchases.

Machine learning can also be used in chatbots. Such systems use keywords in a customer’s query, prompts and yes/no questions to guide customers to the in­for­ma­tion they are looking for. With deep learning, however, chatbots are capable of un­der­stand­ing natural language and do not need to depend on the use of specific keywords. This makes their in­ter­ac­tions with people much more efficient, and sig­nif­i­cant­ly increases the accuracy of the solutions they provide.

Digital voice as­sis­tants like Siri, Alexa und Google almost always use speech synthesis and deep learning nowadays. These digital as­sis­tants are also making their way into business en­vi­ron­ments, where users can use natural language to interact with them to perform a range of tasks, including placing orders, sending emails, creating reports and con­duct­ing research. Earlier systems based on machine learning were not capable of un­der­stand­ing nuances in human speech, making them less effective for such use cases.

While machine learning can be used in the realm of business in­tel­li­gence to visualize important company data and make forecasts easier to un­der­stand for decision-makers, deep learning systems go a step further. For example, with gen­er­a­tive AI, busi­ness­es can create custom graphics and images with simple prompts. Likewise, large language models and natural language pro­cess­ing, both of which use deep learning al­go­rithms, are also helpful for content creation.