Deep learning
Deep learning is a sub-field of machine learning based on artificial neural networks with many layers. These deep networks learn to recognise complex patterns in large amounts of data by themselves, without relevant features having to be specified by hand. Deep learning forms the technical basis of many modern AI applications such as speech, image and text recognition.
Also known as: deep learning, deep machine learning
How does deep learning work?
Deep learning processes data in a network of many layers of artificial neurons one behind another. Each layer extracts increasingly abstract features: in image recognition the first layer recognises simple edges, middle layers shapes and the last layers whole objects. This hierarchical processing allows very complex relationships to be represented.
Learning happens through a method called backpropagation. The network compares its prediction with the desired result, calculates the error and adjusts the connection weights step by step to minimise it. Across many training runs the model's accuracy improves continuously, provided enough data and computing power are available.
How does deep learning differ from classic machine learning?
The central difference lies in feature extraction. In classic ML methods the relevant features often have to be defined by hand, which takes a lot of expertise and effort. Deep learning takes that step automatically and learns the useful features straight from the raw data.
That advantage has its price, though: deep learning models need considerably more training data and substantial computing capacity, often in the form of specialised graphics processors. Their decisions are also harder to follow. With smaller amounts of data or well-structured problems, classic ML methods are therefore often the more efficient choice.
Which network architectures are there?
Different architectures have become established depending on the task. Convolutional neural networks specialise in processing images and underpin modern image recognition. Recurrent networks were long used for sequential data such as speech and text.
For some years, however, the dominant approach has been Transformer architecture the field. It processes relationships in data particularly efficiently and underpins today’s large language models and generative AI systems. This development has turned deep learning from a specialist tool into a broadly usable key technology.
Where is deep learning used?
Deep learning sits behind many technologies that have become everyday. That includes voice assistants, automatic translation, facial recognition, medical image analysis and autonomous driving. Generative applications producing text, images or code rest entirely on deep neural networks too.
For companies, deep learning offers potential above all where large amounts of unstructured data such as images, audio or free text have to be processed. Tasks that once required a lot of manual work can thus be automated and scaled, analysing documents or quality control in manufacturing for instance.
Using deep learning with Elisabit
Deep learning unfolds its value only when the technology is tailored sensibly to concrete business requirements. Elisabit helps companies assess the potential of deep neural networks realistically and implement fitting solutions, from connecting pre-trained models to integrating generative AI into existing applications.
Throughout we value practical results over technical gimmicks. Companies thus benefit from the strengths of deep learning without getting lost in complexity, and lay the ground for sustained digital innovation.
Frequently asked questions
What is the difference between deep learning and machine learning?
Deep learning is a special form of Machine learningbased on deep neural networks. While classic ML methods often depend on manually defined features, deep learning learns these from the raw data itself. Deep learning needs more data and computing power for that, though.
Why does deep learning need so much computing power?
Deep neural networks consist of millions or billions of parameters adjusted during training. These calculations require substantial computing capacity, which is why specialised graphics processors or cloud infrastructure are often used. Using finished models, though, takes considerably less effort than training them.
Is deep learning always the best choice?
No. Deep learning plays to its strengths above all with large, complex and unstructured data. With smaller or well-structured datasets, simpler machine learning methods often give comparable or better results with less effort.
What role does deep learning play in generative AI?
Generative AI rests entirely on deep learning, particularly the transformer architecture. Large language models and image generators are deep neural networks that have learned from vast amounts of data to produce new content. Without deep learning these applications would not be possible.
Related terms
Machine learning enables systems to learn from data and make predictions without being explicitly programmed.
A neural network is a computational model of connected neurons, modelled on the brain, that learns from data.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
The transformer is an AI architecture that captures relationships within text using the attention mechanism.
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
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