Blog 10
What is deep learning?
Gizem Baruk I 16.09.2021
Gizem Baruk I 16.09.2021
Deep learning can be classified in the research field of machine learning and is a special method of information processing. The functionality used by deep learning acts in a similar way to the human brain; it uses neural networks to analyze large data sets. Accordingly, the system can access the neural network and existing information; for example, it can enrich and link previously learned skills with new content. This allows a long-term and in-depth learning process to be mapped out. Deep learning enables the machine to make decisions independently, create forecasts and question decisions made. This technology is particularly suitable for applications based on large data sets to support employees in their work.
How does deep learning work?
Deep learning enables machines to improve and learn new skills without human intervention. The system extracts patterns and computational models from existing data in order to then link findings with correlated and appropriate context. Ultimately, the machine can make decisions based on the context gained. Questioning the decisions made helps to ensure that the information links receive certain weightings. Confirmed decisions increase the weighting, while revised decisions lead to a reduction in the weighting. This approach results in numerous levels between the input and output layers.
and intermediate layers. These are responsible for the connection and ultimately for the output. The name component "deep" refers to the hidden layers of the resulting neural network.
It is interesting, for example, that classical neural networks consist of only two or three hidden networks, whereas deep neural networks can consist of 150 or more hidden layers.
What is the difference between deep learning and machine learning?
Both deep learning and machine learning belong to the thematic area of artificial intelligence.
• Machine Learning: Machine learning differs from deep learning even in the initial workflow. In machine learning, for example, the relevant features must be extracted manually. The system then uses these extracted features to create a model.
Both deep learning and machine learning belong to the thematic area of artificial intelligence.
• Machine Learning: Machine learning differs from deep learning even in the initial workflow. In machine learning, for example, the relevant features must be extracted manually. The system then uses these extracted features to create a model.
• Deep Learning: Deep learning is a subfield of machine learning.
In the modern deep learning workflow, the required features are extracted automatically without human intervention. In addition, the deep learning algorithms are characterized by scalability based on the available data. The larger the data basis, the better the neural network and the resulting decisions.
In the modern deep learning workflow, the required features are extracted automatically without human intervention. In addition, the deep learning algorithms are characterized by scalability based on the available data. The larger the data basis, the better the neural network and the resulting decisions.
