sügavõppimise

Sügavõppimine is the Estonian term for deep learning, a subfield of machine learning that focuses on artificial neural networks with multiple layers. These networks are designed to learn and represent data at various levels of abstraction, mimicking the structure and function of the human brain. Unlike traditional machine learning algorithms that often require manual feature engineering, deep learning models can automatically discover and extract relevant features from raw data.

The architecture of deep learning models, particularly deep neural networks, allows them to process complex patterns in data such as images, sound, and text. Each layer in the network transforms the input data into a more abstract representation. For example, in image recognition, early layers might detect edges and simple shapes, while deeper layers can identify more complex features like objects or faces.

Deep learning has achieved remarkable success in a wide range of applications. These include computer vision tasks like image classification and object detection, natural language processing for tasks such as machine translation and sentiment analysis, and speech recognition. Its ability to handle large datasets and learn intricate relationships has made it a driving force behind many recent advancements in artificial intelligence. The development of powerful hardware, such as GPUs, and the availability of vast amounts of data have been crucial for the progress and widespread adoption of deep learning techniques.