Sügavõppimine

Sügavõppimine, known in English as deep learning, is a subfield of machine learning that utilizes artificial neural networks with multiple layers to learn representations of data. These networks, inspired by the structure and function of the human brain, are capable of learning complex patterns and hierarchical features from raw data.

The core idea behind deep learning is to progressively extract higher-level features from input data. For instance, in image recognition, early layers might detect simple edges, while deeper layers combine these edges to recognize shapes, then objects, and finally entire scenes. This hierarchical learning process eliminates the need for manual feature engineering, which was a significant bottleneck in traditional machine learning.

Deep learning models are trained on vast amounts of data using algorithms like backpropagation. The performance of these models is highly dependent on the size and quality of the training dataset, as well as the architecture of the neural network. Common architectures include convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data such as text and speech.

Applications of deep learning are widespread and continue to expand. They power advancements in areas like autonomous driving, natural language processing, medical diagnosis, recommendation systems, and scientific research. Despite its successes, deep learning also faces challenges, including the need for significant computational resources, the "black box" nature of some models, and concerns regarding bias in training data.