A convolutional neural network (CNN) is a neural network architecture particularly suited to image data. It scans the input with small filters, detecting simple features such as edges in the first layers and more complex shapes in deeper ones. CNNs shaped image recognition for many years and are used, for example, to spot defects in product photos or in medical image analysis.
Deep learning is a branch of machine learning that uses artificial neural networks with many layers. Such networks learn from large amounts of data which features matter – from edges in images to relationships of meaning in texts. Deep learning underpins today's language models, image and speech recognition and machine translation, and usually requires considerable computing power and training data.
Computer vision is a field of AI in which computers interpret images and video. Typical tasks include detecting and locating objects, reading text in images and measuring components. The technology is used, for example, in quality control in manufacturing, in driver assistance systems, in logistics and in medical imaging.
Image recognition is a field of computer vision in which software identifies what an image shows – objects, people, text, logos or scenes. It is usually based on neural networks trained on large numbers of labelled images. Applications range from quality control in manufacturing and sorting product photos to supporting medical imaging. With images of people, data protection and potential bias require particular attention.
Artificial neural networks (ANNs) are computational models made up of many connected units (“neurons”) arranged in layers that process inputs via weighted connections. During training, the weights are adjusted step by step using examples, usually by backpropagation. ANNs are the foundation of deep learning and power image recognition, speech recognition and large language models. Despite their biological inspiration, they work far more simply than the human brain.
