süvaõppevõrkude

Süvaõppevõrgud, also known as deep learning networks, are a subset of machine learning that utilizes artificial neural networks with multiple layers to analyze and learn from data. These networks are inspired by the structure and function of the human brain, with interconnected nodes (neurons) organized into layers. The "deep" aspect refers to the presence of numerous hidden layers between the input and output layers, allowing the network to learn increasingly complex representations of data.

The process of training a süvaõppevõrk involves feeding it large amounts of labeled data. During training, the network adjusts the weights of the connections between neurons to minimize the difference between its predicted output and the actual desired output. This iterative adjustment process, often using algorithms like backpropagation, enables the network to identify intricate patterns and features that might be imperceptible to humans or simpler machine learning models.

Süvaõppevõrgud have achieved remarkable success in a wide range of applications. These include image recognition, where they can identify objects and classify images with high accuracy; natural language processing, enabling tasks like machine translation, sentiment analysis, and text generation; and speech recognition, powering virtual assistants and transcription services. Their ability to automatically learn relevant features from raw data has made them a powerful tool in fields such as autonomous driving, medical diagnosis, and scientific research.