Sügavusõpetus

Sügavusõpetus, also known as deep learning, is a subset of machine learning that involves the use of artificial neural networks with many layers to model complex patterns in data. These networks are designed to automatically learn and represent features from raw data, which can then be used for tasks such as image and speech recognition, natural language processing, and more.

The term "deep" refers to the number of layers in the neural network. Traditional machine learning algorithms often require manual feature extraction, where human experts identify and select relevant features from the data. In contrast, deep learning algorithms can learn these features directly from the raw data, making them particularly powerful for tasks involving large and complex datasets.

Deep learning has achieved significant success in various domains, including computer vision, where it has been used to develop advanced image recognition systems, and natural language processing, where it has enabled the creation of sophisticated language models. However, deep learning also presents challenges, such as the need for large amounts of labeled data and the computational resources required to train deep neural networks.

The field of deep learning is rapidly evolving, with ongoing research focused on improving the efficiency and scalability of deep learning models, as well as exploring new architectures and techniques to address its limitations. As a result, deep learning continues to be a highly active area of study in artificial intelligence and machine learning.