süvaõppes

Süvaõppes, meaning deep learning in Estonian, refers to a subset of machine learning that utilizes artificial neural networks with multiple layers to learn complex patterns from data. Unlike traditional machine learning algorithms that often require manual feature engineering, deep learning models can automatically extract hierarchical features from raw input. This capability makes them particularly effective for tasks involving unstructured data such as images, audio, and text.

The core concept behind deep learning is the layered structure of artificial neural networks, inspired by the human brain. Each layer in a deep neural network processes information from the previous layer and transforms it into a more abstract representation. As data passes through successive layers, the network learns increasingly sophisticated features. For instance, in image recognition, early layers might detect edges and simple shapes, while deeper layers can identify more complex objects and scenes.

Deep learning has achieved state-of-the-art results in numerous applications. These include image and speech recognition, natural language processing (such as machine translation and text generation), autonomous driving, medical diagnosis, and scientific research. The success of deep learning is largely attributed to the availability of large datasets and advancements in computational power, particularly the use of graphics processing units (GPUs) for parallel processing. Despite its successes, deep learning models can be computationally expensive to train and often require significant amounts of data, posing challenges for certain real-world scenarios.