géptani

Géptani is a Hungarian term that translates to "machine learning" in English. It refers to a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that computer systems can use to perform a specific task without using explicit instructions, relying on patterns and inference instead. Essentially, géptani enables computers to learn from data.

The core idea behind géptani is that systems can automatically learn and improve from experience without being explicitly programmed. This learning process typically involves feeding large amounts of data into an algorithm, which then identifies patterns, makes predictions, or takes actions based on that data. There are several major types of géptani, including supervised learning, where algorithms are trained on labeled datasets, unsupervised learning, which involves finding patterns in unlabeled data, and reinforcement learning, where algorithms learn through trial and error by receiving rewards or penalties.

Applications of géptani are widespread and continue to grow. They are found in everyday technologies such as recommendation systems on streaming services and e-commerce sites, spam filters in email, voice assistants like Siri and Alexa, and image recognition software. In more specialized fields, géptani is used for medical diagnosis, financial forecasting, autonomous vehicle navigation, and scientific research. The continuous advancement of computational power and the availability of vast datasets are key drivers behind the rapid progress and increasing sophistication of géptani.