What is the dif­fer­ence between deep learning and machine learning?

Machine learning and deep learning enable AI systems to learn from data and identify patterns. The key dif­fer­ence is that machine learning covers a range of methods and model types, including re­gres­sion models, decision trees, support vector machines and neural networks. Deep learning, on the other hand, is a spe­cial­ised approach based on deep neural networks with many layers.

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

Both machine learning and deep learning are subfields of ar­ti­fi­cial in­tel­li­gence. Both ap­proaches are methods for training AI systems. They process large volumes of data, identify patterns and adjust their models to recognise new patterns, allowing them to make pre­dic­tions or decisions based on the in­form­a­tion they learn. Deep learning is a subset of machine learning that uses multi-layer neural networks.

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Neural networks

To better un­der­stand the dif­fer­ence between deep learning and machine learning, it helps to first look at neural networks. Neural networks are an ar­chi­tec­ture loosely inspired by the human brain. They consist of many in­ter­con­nec­ted pro­cessing units that handle in­form­a­tion step by step. These pro­cessing stages are known as ‘layers’. Neural networks generally consist of three types of layers:

• Input layer: This is where data enters the network.
• Hidden layers: These layers process the input data and extract patterns.
• Output layer: This is where the final result is produced.

The input layer and output layer mark the beginning and end of a neural network. Between them are the so-called hidden layers. Their structure can vary depending on the com­plex­ity of the model: simpler networks often have only one or a few hidden layers, while modern models can contain many layers and millions to trillions of para­met­ers. Each layer performs specific cal­cu­la­tions based on the values from the previous layer and passes the results on to the next.

These layers can be thought of as several levels of pro­cessing. Each level iden­ti­fies different features in the data. In an image, for example, an early layer might detect simple lines or edges, another layer might recognise shapes, and later layers might identify more complex struc­tures such as eyes, ears or fur.

During training, the con­nec­tions and weights between the in­di­vidu­al layers are adjusted until the output layer produces reliable results. This allows the system to learn tasks such as re­cog­nising whether an image shows a dog or a cat.

Machine learning

Machine learning describes an approach to ar­ti­fi­cial in­tel­li­gence where systems learn from data and improve their per­form­ance without needing every rule to be ex­pli­citly pro­grammed. It refers to a method of ar­ti­fi­cial in­tel­li­gence in which computers are not pro­grammed with every single rule in advance. Instead, the system is trained using data and learns to identify patterns. Based on these patterns, it can later classify new data, make pre­dic­tions or support decision-making.

Machine learning can use various methods, including decision trees, re­gres­sion models and neural networks. When neural networks are used in tra­di­tion­al machine learning, they are typically less complex and contain only a few layers. As a result, tra­di­tion­al machine learning is often suitable for tasks where the amount of data is more man­age­able and relevant features can be iden­ti­fied and prepared more easily.

Deep learning

Deep learning is a spe­cial­ised form of machine learning that takes this approach a step further. Like tra­di­tion­al machine learning, it trains systems using data so they can identify patterns and classify new in­form­a­tion. The key dif­fer­ence lies in how these patterns are processed: deep learning uses ar­ti­fi­cial neural networks with many layers. As a general guideline, neural networks are often con­sidered deep learning models when they contain more than the three basic layers of a simple network. However, there is no uni­ver­sally defined minimum number of layers.

The main advantage of deep learning is that its many layers allow the system to auto­mat­ic­ally learn relevant features from data. In tra­di­tion­al machine learning, these features usually need to be selected or prepared be­fore­hand. This makes deep learning par­tic­u­larly suitable for complex tasks involving large amounts of data, such as image re­cog­ni­tion, speech re­cog­ni­tion, automatic trans­la­tion and gen­er­at­ive AI. Thanks to pre-trained models and transfer learning, deep learning can also be applied ef­fect­ively to smaller, spe­cial­ised datasets, as pre­vi­ously learned patterns can be trans­ferred to new tasks and adapted ac­cord­ingly.

An overview of the key dif­fer­ences between deep learning and machine learning

Where are machine learning and deep learning used?

Online marketing

In the field of online marketing, companies often use marketing analytics tools based on machine learning. These tools analyse existing data and can provide reliable forecasts about which types of content drive con­ver­sions, what content customers are in­ter­ested in and which marketing channels are most likely to lead to purchases.

Customer support

Customer support chatbots can also be powered by machine learning and deep learning. Simpler chatbots often rely on pre­defined rules, keywords or decision trees. They identify specific terms in a query and guide users step by step towards a suitable answer.

Modern AI chatbots, on the other hand, often use natural language pro­cessing, Large Language Models and deep learning methods. These models are typically developed in several stages. Broad pre-training is often followed by fine-tuning and further ad­just­ments to adapt responses to a specific use case. This allows them to process natural language more ef­fect­ively, un­der­stand re­la­tion­ships within queries and respond more flexibly to different wording.

Business in­tel­li­gence

In the field of business in­tel­li­gence, machine learning can help analyse company data, identify patterns, detect anomalies and generate forecasts. This makes it easier, for example, to evaluate sales trends, predict demand, un­der­stand customer behaviour and assess risks. Deep learning becomes par­tic­u­larly useful when very large volumes of data or un­struc­tured data, such as text, images, speech or complex be­ha­vi­our­al in­form­a­tion, need to be processed.