süvaõppega

Süvaõppega, also known as deep learning, is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the human brain, known as artificial neural networks. These networks consist of multiple layers of interconnected nodes, or neurons, which process and transform data. Unlike traditional machine learning methods that require manual feature engineering, deep learning models learn to extract relevant features directly from raw data.

The "deep" in deep learning refers to the number of layers in the neural network. A network with more layers can learn more complex and hierarchical representations of data. For instance, in image recognition, early layers might detect simple features like edges and corners, while deeper layers can combine these to recognize more complex patterns like shapes, objects, and even entire scenes.

Deep learning has achieved state-of-the-art results in a wide range of applications, including image and speech recognition, natural language processing, autonomous driving, and medical diagnosis. Its success is largely attributed to the availability of large datasets and advancements in computational power, particularly through the use of graphics processing units (GPUs).

Despite its successes, deep learning also presents challenges. Training deep neural networks can be computationally expensive and requires significant amounts of data. Furthermore, understanding why a deep learning model makes a particular decision can be difficult, leading to concerns about interpretability and bias. Research continues to address these limitations and explore new architectures and training methodologies.